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Досліджено застосування байєсівського підходу до оцінювання параметрів математичних моделей та методу аналізу статистичних даних у страхуванні на основі узагальнених лінійних моделей, які являють собою розширення лінійної регресії на випадки, коли розподіл випадкових величин може відрізнятись від нормального. Це дає можливість докладніше описати структуру та зміст досліджуваної моделі. Процедура оцінювання параметрів моделі виконується із використанням класичного методу та байєсівського підходу. На основі статистичних даних стосовно величини збитків у сфері автомобільного страхування побудовано модель для прогнозування цього актуарного процесу. Прийнятною для подальшого використання виявилась модель із законом розподілу Пуассона та експоненціальною функцією зв’язку. Це підтверджується мінімальною величиною похибки, а також достовірною оцінкою параметрів узагальнених лінійних моделей, отриманих із використанням байєсівського підходу. Встановлено, що нормальна модель з тотожною функцією зв’язку дає можливість отримати результат за одну ітерацію з незначною відносною похибкою, але недостатньо точними прогнозними значеннями збитків. The article deals with Bayesian methodology for estimating unknown parameters of mathematical models and the method of analysis statistic data in insurance based on generalized linear models. These models are extension of linear regression when distribution of random variable can differ from normal. For estimating the parameters of proposed models classical and Bayesian approach were used. The main advantage of Bayesian approach is its ability to generate not only accurate estimates but probability distributions too. It gives the opportunity to describe in details the structure and the nature of investigated models. The value of damages in autoinsurance were hired for creating the forecasting model of actuarial process. The model with Poisson distribution and an exponential link function turned out to be acceptable for further use because it has minimum value of observation error and reliable estimate for risk value which was received using Bayesian approach. A normal model with identity link function allows to generate a result after one iteration with small value of observation error but “weak” predicted value of losses and poor risk assessment. Исследовано применение байесовского подхода к задаче оценивания неизвестных параметров моделей и метода анализа статистических данных в сфере страхования на основе обобщенных линейных моделей, которые представляют собой расширение линейной регрессии на случаи, когда распределение случайных величин может отличаться от нормального. Для данной задачи оценивание неизвестных параметров моделей осуществлено c помощью байесовского подхода и метода максимального правдоподобия. На основании статистических данных об убытках в сфере автострахования построена прогнозирующая модель для актуарного процесса. Допустимой для дальнейшего применения оказалась модель с законом распределения Пуассона и экспоненциальной функцией связи. Это обосновывается минимальной величиной погрешности, а также достоверной величиной риска и достоверной оценкой параметров обобщенных линейных моделей с применением байесовского похода. Установлено, что нормальная модель с тождественной функцией связи позволяет получить результат за одну итерацию с допустимым значением относительной погрешности, но “слабым” прогнозным значением убытков и недопустимой оценкой риска.

Determination of sound decay times in coupled spaces often demands considerable effort. Based on Schroeder's backward integration of room impulse responses, it is often difficult to distinguish different portions of multirate sound energy decay functions. A model-based parameter estimation method, using Bayesian probabilistic inference, proves to be a powerful tool for evaluating decay times. A decay model due to one of the authors [N. Xiang, J. Acoust. Soc. Am. 98, 2112-2121 (1995)] is extended to multirate decay functions. Following a summary of Bayesian model-based parameter estimation, the present paper discusses estimates in terms of both synthesized and measured decay functions. No careful estimation of initial values is required, in contrast to gradient-based approaches. The resulting robust algorithmic estimation of more than one decay time, from experimentally measured decay functions, is clearly superior to the existing nonlinear regression approach.

We transform a compartmental model given by a set of deterministic differential equations and a set of discrete system parameters into a system of stochastic differential equations and obtain a mixed graphical model by time discretization. This mixed graphical model allows for Bayesian parameter estimation by introducing partial knowledge about the system. This improved knowledge then may be used for prediction and, hence, optimization of therapy.

We propose Bayesian parameter estimation in a multidimensional item response theory model using the Gibbs sampling algorithm. We apply this approach to dichotomous responses to a questionnaire on sleep quality. The analysis helps determine the underlying dimensions.

Bayesian statistical methods are widely used in probabilistic risk assessment (PRA) because of their ability to provide useful estimates of model parameters when data are sparse and because the subjective probability framework, from which these methods are derived, is a natural framework to address the decision problems motivating PRA. This paper presents a tutorial on Bayesian parameter estimation especially relevant to PRA. It summarizes the philosophy behind these methods, approaches for constructing likelihood functions and prior distributions, some simple but realistic examples, and a variety of cautions and lessons regarding practical applications. References are also provided for more in-depth coverage of various topics.

This paper considers Bayesian parameter estimation and an associated adaptive control scheme for controlled Markov chains and diffu- sions with time-averaged cost. Asymptotic behaviour of the posterior law of the parameter given the observed trajectory is analyzed. This analysis sug- gests a "cost-biased" estimation scheme and associated self-tuning adaptive control. This is shown to be asymptotically optimal in the almost sure sense.

We develop and perform a non reactive A/B-test setting that enables us to evaluate the influence of green marketing signals on the customer's decision to take a specific online-shop into account in the process of buying a specific product by clicking on an ad on a search engine results page (SERP). We analyze campaign performance data generated by a European e-commerce retailer, apply a Bayesian parameter estimation to compare specific advertisements and discuss the implications of the results.

Evaluation of the vulnerability of buildings in urban areas to flooding is a funda-mental step in flooding risk mitigation. In this work, a new GIS-compatible computer platform with Matlab®-based graphical user interface is presented: VISK, "Visual Vulnerability & Risk", flooding module. This platform performs detailed (micro-scale) flood risk assessment for building stock with more-or-less similar characteristics. The GIS compatibility allows for graphical processing of both input and output to the program, providing an efficient visuali-zation of flooding risk. At the core of the platform lies a comprehensive probability-based al-gorithm for the assessment of the vulnerability of a class of buildings to flooding. This Bayesian algorithm is based on assigning prescribed analytic uni- and bi-modal probability distributions for characterizing the flooding structural fragility functions. This allows for effi-cient evaluation of structural fragility based on a small number of (around 30-50) Monte Car-lo simulations. The fragility calculations are performed on a bi-dimensional finite-element structural model considering the openings (door and windows) constructed using open-source software Opensees. The uncertain structural modeling parameters are characterized through, orthophoto recognition, sample in-situ building survey, laboratory test results for material mechanical properties and literature survey. Finally, the risk map is generated by integrating the flooding hazard and fragility taking into account additional information on the exposure (e.g., repair costs, population density, etc.). The results can be visualized both in a detailed building-to-building scale (of potential interest to single house-holds) or as overall estimates for the entire area (of interest to policy makers).

A Bayesian approach to estimating the physical parameters of rigid-frame porous materials through measurements of the acoustic impedance of such materials is presented. Porous materials are widely used as sound absorbers in many industries. Modeling the effects of porous absorbers and making optimal use of porous absorbent materials requires knowledge of the physical parameters characterizing such materials: porosity, tortuosity, and flow resistivity. For many materials, direct measurement of these parameters requires time-consuming or highly sensitive procedures. Based on some existing models for the characteristic impedance or propagation coefficient of a porous material in terms of the physical material parameters, Bayesian parameter estimation is used to estimate the physical parameters of a material from a measurement of its complex acoustic impedance or propagation coefficient. In addition to estimation of the values of the physical parameters, Bayesian analysis provides information on the uncertainties and interdependence of the parameters.

The utility of a Bayesian parameter estimation program in the interpretation of cyclosporin concentrations was investigated in a group of 32 patients following renal transplantation. The program was evaluated by comparing concentrations predicted from individual estimates of pharmacokinetic parameters with measured concentrations. A one-compartment model incorporating an exponential, time-related change in clearance was used for data collected in the first 4 weeks after transplantation, and predictions of concentrations measured during weeks 5-14 were made using three schemes: a changing clearance model using all data from week 1 onward; a changing clearance model using data from week 4 onward; and a nonchanging clearance model using data from week 4 onward. Results demonstrated that predictions made by the Bayesian program were unreliable during the first 4 weeks of therapy, but that there was a progressive improvement as time after transplantation increased. The changing clearance model was superior to the constant clearance model and its performance was not compromised by including data from the first 4 weeks of therapy. Although the Bayesian approach may help with the interpretation of blood cyclosporin concentrations during maintenance therapy, the large variability in the pharmacokinetics of orally administered cyclosporin limits the usefulness of the approach in the early weeks following transplantation.

One of the most popular paradigms to use for studying human reasoning involves the Wason card selection task. In this task, the participant is presented with four cards and a conditional rule (e.g., “If there is an A on one side of the card, there is always a 2 on the other side”). Participants are asked which cards should be turned to verify whether or not the rule holds. In this simple task, participants consistently provide answers that are incorrect according to formal logic. To account for these errors, several models have been proposed, one of the most prominent being the information gain model (Oaksford & Chater, Psychological Review, 101, 608–631, 1994). This model is based on the assumption that people independently select cards based on the expected information gain of turning a particular card. In this article, we present two estimation methods to fit the information gain model: a maximum likelihood procedure (programmed in R) and a Bayesian procedure (programmed in WinBUGS). We compare the two procedures and illustrate the flexibility of the Bayesian hierarchical procedure by applying it to data from a meta-analysis of the Wason task (Oaksford & Chater, Psychological Review, 101, 608–631, 1994). We also show that the goodness of fit of the information gain model can be assessed by inspecting the posterior predictives of the model. These Bayesian procedures make it easy to apply the information gain model to empirical data. Supplemental materials may be downloaded along with this article from www.springerlink.com. Electronic supplementary material The online version of this article (doi:10.3758/s13428-010-0057-5) contains supplementary material, which is available to authorized users.

A recent trend in decision neuroscience is the use of model-based fMRI using mathematical models of cognitive processes. However, most previous model-based fMRI studies have ignored individual differences due to the challenge of obtaining reliable parameter estimates for individual participants. Meanwhile, previous cognitive science studies have demonstrated that hierarchical Bayesian analysis is useful for obtaining reliable parameter estimates in cognitive models while allowing for individual differences. Here we demonstrate the application of hierarchical Bayesian parameter estimation to model-based fMRI using the example of decision making in the Iowa Gambling Task. First we use a simulation study to demonstrate that hierarchical Bayesian analysis outperforms conventional (individual- or group-level) maximum likelihood estimation in recovering true parameters. Then we perform model-based fMRI analyses on experimental data to examine how the fMRI results depend upon the estimation method.

In this paper, a new method for Bayesian periodic parameter estimation is derived using periodic cost functions. The estimation procedure is evaluated using Fourier series representation of the periodic cost functions. The minimum cyclic and the minimum periodic mean-square-error (MSE) estimators are derived. The proposed periodic estimators are applied to direction-of-arrival (DOA) estimation problem and their performance are compared with the minimum mean-square-error (MMSE) and maximum a-posteriori probability (MAP) estimators in terms of periodic MSE (PMSE).

A main scientific output of the LISA Pathfinder mission is to provide a noise model that can be extended to the future gravitational wave observatory, LISA. The success of the mission depends thus upon a deep understanding of the instrument, especially the ability to correctly determine the parameters of the underlying noise model. In this work we estimate the parameters of a simplified model of the LISA Technology Package (LTP) instrument. We describe the LTP by means of a closed-loop model that is used to generate the data, both injected signals and noise. Then, parameters are estimated using a Bayesian framework and it is shown that this method reaches the optimal attainable error, the Cramer-Rao bound. We also address an important issue for the mission: how to efficiently combine the results of different experiments to obtain a unique set of parameters describing the instrument., 14 pages, 4 figures, submitted to PRD

This study deals with the overcapacity problem of water treatment plants in Korea, and mainly discusses status, causes, and engineering options. To this end, we first statistically analyze the recent trend of demand, revealing that the demands of small- and mid-size systems are still increasing while that of large-size systems is now decreasing. Since the existing approach to plan capacity implicitly assumes that demand will increase at a regular rate, we estimate excess capacities and system utilizations of large-size systems. From these results it is found that the large-size systems are suffering from serious overcapacity, thus necessitating that engineers make very difficult decisions given that systems are still expanding the capacities of plants due to a lack of awareness of the current demand trend. For other systems where there is a better understanding of the transition of demand, planners have ceased to expand plants or have closed down relatively old plants in efforts to reduce O&M costs. To address this problem, quick recognition of the transition of demand is being highlighted by the concepts of integrated resources management and cybernetics. Therefore, we examined how quickly the new trend of the Seoul case could be precisely recognized and appropriately addressed. Using the Bayesian parameter estimation method, we found that a new trend can be recognized six years after the transition of demand.

International audience; One common limitation of the use of crop models for decision making in precise crop management is the need for accurate values of soil parameters for a whole field. Estimating these parameters from data observed on the crop, using a crop model, is an interesting possibility. Nevertheless, the quality of the estimation depends on the sensitivity of model output variables to the parameters. The goal of this study is to explain the results for the quality of parameter estimation based on global sensitivity analysis (GSA). The case study consists of estimating the soil parameters by using the STICS-wheat crop model and various synthetic observations on wheat crops (LAI, absorbed nitrogen and grain yield). Suitable criteria summarizing the sensitivity indices of the observed variables were created in order to link GSA indices with the quality of parameter estimation. We illustrate this link on 16 different configurations of different soil, climatic and crop conditions. The GSA indices were computed by the Extended FAST method and a function of RMSE was computed with an importance sampling method based on Bayes theory (GLUE). The proposed GSA-based criteria are able to rank the parameters with respect to their quality of estimation and the different configurations (especially climate and observation set) with respect to their ability to estimate the whole parameter set. They may be used as a tool for predicting the performance of different observation datasets with regard to parameter estimation

Dynamic crop models are very useful to predict the behavior of crops in their environment and are widely used in a lot of agro-environmental work. These models have many parameters and their spatial application require a good knowledge of these parameters,especially of the soil parameters. These parameters can be estimated from soil analysis at different points but this is very costly and requires a lot of experimental work. Nevertheless,observations on crops provided by new techniques like remote sensing or yield monitoring, is a possibility for estimating soil parameters through the inversion of crop models. In my work, the STICS crop model is studied for the wheat and the sugar beet and it includes more than 200 parameters. After a previous work based on a large experimental database for calibrate parameters related to the characteristics of the crop, I started my study with a global sensitivity analysis of the observed variables (leaf area index LAI and absorbed nitrogen QN provided by remote sensing data, and yield at harvest provided by yield monitoring) to the soil parameters, in order to determine which of them have to be estimated. This study was made in different climatic and agronomic conditions and it reveals that 7 soil parameters (4 related to the water and 3 related to the nitrogen) have a clearly influence on the variance of the observed variables and have to be therefore estimated. For estimating these 7 soil parameters, I chose a Bayesian data assimilation method (because I have prior information on these parameters) named Importance Sampling by using observations, on wheat and sugar beet crop, of LAI and QN at various dates and yield at harvest acquired on different climatic and agronomic conditions. The quality of parameter estimation is then determined by comparing the result of parameter estimation with only prio rinformation and the result with the posterior information provided by the Bayesian data assimilation method. The result of the parameter estimation show that the whole set of parameter has a better quality of estimation when observations on sugar beet are assimilated. At the same time, global sensitivity analysis of the observed variables to the 7 soil parameters have been performed, allowing me to build a criterion based on sensitivity indices (provided by the global sensitivity analysis) able to rank the parameters with respect to their quality of estimate. This criterion constitutes an interesting tool for determining which parameters it is possible to estimate to reduce probably the uncertainties on the predictions. The prediction of the crop behaviour when estimating the soil parameters is then studied. Indeed, the quality of prediction of agro-environmental variables of the STICS crop model (yield, protein of the grain and nitrogen balance at harvest) is determined by comparing the result of the prediction using the prior information on the parameters and the result using the posterior information. As for the estimation of soil parameters, the prediction of the variable is made on different climatic and agronomic conditions. According to the result of parameter estimation, assimilating observations on sugar beet lead to a better quality ofprediction of the variables than observations on wheat. It was also shown that the number ofcrop seasons observed and the number of observations improve the quality of the prediction, Les modèles de culture constituent des outils indispensables pour comprendre l’influence des conditions agropédoclimatiques sur le système sol-plante à différentes échelles spatiales et temporelles. A l’échelle locale de la parcelle agricole, le modèle peut être utilisé dans le cadre de l’agriculture de précision pour optimiser les pratiques de fertilisation azotée de façon à maximiser le rendement ou le revenu tout en minimisant le lessivage des nitrates vers la nappe. Cependant, la pertinence de l’utilisation du modèle repose sur la qualité des prédictions réalisées, basée entre autres sur une bonne détermination des paramètres d’entrée du modèle. Dans le cadre de l’agriculture de précision, les paramètres concernant les propriétés des sols sont les plus délicates à connaître en tout point de la parcelle et il existe très peu de cartes de sols permettant de les déterminer de manière précise. Néanmoins, dans ce contexte, on peut disposer d’observations acquises automatiquement sur l’état du système sol-plante, telles que des images de télédétection, les cartes de rendement ou les mesures de résistivité électrique du sol. Il existe alors une alternative intéressante pour estimer les propriétés des sols à l’échelle de la parcelle qui consiste à inverser le modèle de culture à partir de ces observations pour retrouver les valeurs des propriétés des sols. L’objectif de cette thèse consiste (i) dans un premier temps à analyser les performances d’estimation des propriétés des sols par inversion du modèle STICS à partir de différents jeux d’observations sur des cultures de blé et de betterave sucrière, en mettant en oeuvre une méthode bayésienne de type Importance Sampling, (ii) dans un second temps à mesurer l’amélioration des prédictions de variables agro-environnementales réalisées par le modèle à partir des valeurs estimées des paramètres. Nous montrons que l’analyse de sensibilité globale permet de quantifier la quantité d’information contenue dans les jeux d’observations et les performances réalisées en matière d’estimation des paramètres. Ce sont les propriétés liées au fonctionnement hydrique du sol (humidité à la capacité au champ, profondeur de sol, conditions initiales) qui bénéficient globalement de la meilleure performance d’estimation par inversion. La performance d’estimation, évaluée par comparaison avec l’estimation fournie par l’information a priori, dépend fortement du jeu d’observation et est significativement améliorée lorsque les observations sont faites sur une culture de betterave, les conditions climatiques sont sèches ou la profondeur de sol est faible. Les prédictions agro-environnementales, notamment la quantité et la qualité du rendement, peuvent être grandement améliorées lorsque les propriétés du sol sont estimées par inversion, car les variables prédites par le modèle sont également sensibles aux propriétés liées à l’état hydrique du sol. Pour finir, nous montrons dans un travail exploratoire que la prise en compte d’une information sur la structure spatiale des propriétés du sol fournie par les mesures de résistivité électrique, peut permettre d’améliorer l’estimation spatialisée des propriétés du sol. Les observations acquises automatiquement sur le couvert végétal et la résistivité électrique du sol se révèlent être pertinentes pour estimer les propriétés du sol par inversion du modèle et améliorer les prédictions des variables agro-environnementales sur lesquelles reposent les règles de choix des pratiques agricoles

This paper presents and discusses the use of Bayesian procedures - introduced through the use of Bayesian networks in Part I of this series of papers - for 'learning' probabilities from data. The discussion will relate to a set of real data on characteristics of black toners commonly used in printing and copying devices. Particular attention is drawn to the incorporation of the proposed procedures as an integral part in probabilistic inference schemes (notably in the form of Bayesian networks) that are intended to address uncertainties related to particular propositions of interest (e.g., whether or not a sample originates from a particular source). The conceptual tenets of the proposed methodologies are presented along with aspects of their practical implementation using currently available Bayesian network software.

Date du colloque: 01/2009; International audience; This paper provides an overview of the application of Bayesian inference to accelerated life testing (ALT) models for the concrete case of estimation by Maximum of Aposteriori (MAP) method in the case of constant stress levels. It studies the Bayesian inference over the accelerated life model as presented in [1]. It suites, integrates and generalizes the particular cases presented in [2] and [3]. Towards the end, weighting of the prior information according to data is integrated. The paper also illustrates an experimental example.

A sequential Bayesian parameter estimation technique ensures that a minimum of experimental thermodynamic and phase diagram data suffices for the calculation of excess functions which are comparable in accuracy and precision with quantities obtained by conventional methods. In addition, the new algorithm provides a feedback between data evaluation and experimental design. Typical applications of the proposed method are illustrated by several examples.

This paper constructs an extensive experimental database that includes all the types of reinforced concrete (RC) beam-column connections found in the literature to have experimental joint shear failure. Three kinds of RC joint shear strength models are developed using this database in conjunction with a Bayesian parameter estimation method. The improved performance of the developed joint shear strength models is evaluated by comparison with other deterministic RC joint shear strength models from the literature and codes. A modified ACI 352R-02 joint shear strength definition for design, with adjusted joint shear strength factors for greater consistency, is suggested.

An approach for visual information analysis and classification is presented. It is based on a knowledge synthesizing technique to automatically create a relevance map from essential areas in natural images. It also derives a set of well-structured representations from low-level description to drive the final classification. The backbone of this approach is a distribution mapping strategy involving a knowledge synthesizing module based on an intelligent growing when required network. Classification is achieved by simulating the high-level top-down visual information perception in primates followed by incremental Bayesian parameter estimation. The proposed modular system architecture offers straightforward expansion to include user relevance feedback, contextual input, and multimodal information if available.

In this paper we will discuss two problems related to action recognition: The first problem is the one of identifying in asurveillance scenario whether a person is walking or running and in what rough direction. The second problem is concerned with the recovery of action primitives from observed complex actions. Both problems will be discussed within a statistical framework. Bayesian propagation over time offers a framework to treat likelihood observations at each time step and the dynamics between the time steps in a unified manner. The first problem will be approached as a patter recognition and tracking task by a Bayesian propagation of the likelihoods. The latter problem will be approached by explicitly specifying the dynamics while the likelihood measure will give a measure how good each dynamical model fit at each time step. Extensive experimental results show the applicability of the Bayesian framework for action recognition and round up our discussion.

Presented here are 11 noteworthy papers selected from the Fifth International Conference on Inverse Problems in Engineering: Theory and Practice held in Cambridge, UK during 11–15 July 2005. The papers have been peer-reviewed to the usual high standards of this journal and the contributions of reviewers are much appreciated. The conference featured a good balance of the fundamental mathematical concepts of inverse problems with a diverse range of important and interesting applications, which are represented here by the selected papers. Aspects of finite-element modelling and the performance of inverse algorithms are investigated by Autrique et al and Leduc et al. Statistical aspects are considered by Emery et al and Watzenig et al with regard to Bayesian parameter estimation and inversion using particle filters. Electrostatic applications are demonstrated by van Berkel and Lionheart and also Nakatani et al. Contributions to the applications of electrical techniques and specifically electrical tomographies are provided by Wakatsuki and Kagawa, Kim et al and Kortschak et al. Aspects of inversion in optical tomography are investigated by Wright et al and Douiri et al. The authors are representative of the worldwide interest in inverse problems relating to engineering applications and their efforts in producing these excellent papers will be appreciated by many readers of this journal.

[1] We present a Bayesian parameter estimation technique for improving estimates of simulation model input parameters and apply the technique to the nitrogen cycle model SINIC, which has been used to simulate the streamflow and streamflow nitrate flux at Hubbard Brook Watershed 6, New Hampshire, during the 1964–1994 period. Uncertainty in initial estimates of model input parameters was incorporated by replacing each estimate with a probability distribution of values, or “prior” distribution, usually centered at the initial estimate and having a large variance. These prior distributions were then “updated” by incorporating available data on model output variables, producing a “posterior” probability distribution of parameter values. Several key parameters used for calculating the N mineralization rate were identified as controlling the predicted nitrate export from this watershed. The level of uncertainty in these parameters was substantially reduced by incorporating the observations on streamflow and streamflow nitrate flux. The posterior distribution of predicted yearly streamflow nitrate flux shifted from year to year, with relatively large uncertainties in years with high streamflow nitrate flux.

In this paper, the blind image deconvolution (BID) problem is addressed using the Bayesian framework. In order to solve for the proposed Bayesian model, we present a new methodology based on a variational approximation, which has been recently introduced for several machine learning problems, and can be viewed as a generalization of the expectation maximization (EM) algorithm. This methodology reaps all the benefits of a "full Bayesian model" while bypassing some of its difficulties. We present three algorithms that solve the proposed Bayesian problem in closed form and can be implemented in the discrete Fourier domain. This makes them very cost effective even for very large images. We demonstrate with numerical experiments that these algorithms yield promising improvements as compared to previous BID algorithms. Furthermore, the proposed methodology is quite general with potential application to other Bayesian models for this and other imaging problems. Ieee Transactions on Signal Processing

Neurons in the primary visual cortex are highly selective for stimulus orientation, whereas their thalamic inputs are not. Much controversy has been focused on the mechanism by which cortical orientation selectivity arises. Although an increasing amount of evidence supports a linear model in which orientation selectivity is conferred upon visual cortical cells by the alignment of the receptive fields of their thalamic inputs, the controversy has recently been rekindled with the suggestion that late cortical input--delayed by multiple synapses--could lead to sharpening of orientation selectivity over time. Here we used intracellular recordings in vivo to examine temporal properties of the orientation-selective response to flashed gratings. Bayesian parameter estimation demonstrated that both preferred orientation and tuning width were stable throughout the response to a single stimulus.

The dispositions and dose requirements for vancomycin and gentamicin were investigated in a 58-year-old man who was receiving long-term continuous venovenous hemofiltration. Estimates of clearance were obtained using a Bayesian parameter estimation program and stayed remarkably consistent throughout the therapy. Single daily doses of both vancomycin and gentamicin generally maintained the profiles for both drugs around the target ranges of peak 5–10 mg/L (gentamicin) and 25–40 mg/L (vancomycin) and trough

Micro-perforated panel (MPP) absorbers have been widely accepted in applications as an efficient and clean sound-absorption solution providing extremely high, yet relatively narrow frequency band, acoustic absorption coefficients. Design for the custom acoustic performance of MPPs can be challenging for engineers and designers, however, due to the number of parameters making up the panel absorbers. This work examines the effects of the perforation rate, pore diameter, airspace depth, and panel thickness on the absorption profile of single- and double-layer MPP absorbers. A Bayesian inference framework is implemented in order to inversely determine parameter values and their uncertainties from experimentally measured acoustic absorption data of various MPP constructions of unknown parameters. This analysis also provides insight into the effects of each parameter on the acoustic performance as well as interdependence among the parameters.

The wide use of psychometric assessments and the associated time required to conduct such experiments have motivated a substantial research effort focused on developing more efficient psychometric procedures to expedite the estimation of parameters of interest. Adaptive step size psychometric procedures, which have increasingly received attention as an alternative to fixed step size procedures, can be viewed as having two separate stages: estimation of the parameters of interest based on the available data and a selection of the experimental settings in order to maximize the information obtained from the next trial. Psychometric procedures that utilize a Bayesian framework for parameter estimation have become more prevalent in recent years. In this talk, we will present experimental results for Bayesian psychometric procedures from both psychoacoustics studies and computer simulations. Several computer simulations explored the sensitivity of the procedure to different model parametrizations and evaluated performance using stimulus selection rules based on minimizing entropy and maximizing information gain. Psychoacoustic studies using two different listening tasks were used to investigate the robustness and stability of estimates provided by the Bayesian parameter estimation procedure. Collectively, the outcomes suggest that a Bayesian framework may be an important component in the development of more efficient psychometric procedures.

Streptomycin dose requirements were determined in an 83-year-old man with renal impairment who was being treated for miliary tuberculosis. Concentration measurements were interpreted using a Bayesian parameter estimation program. Estimated creatinine clearance (1.1 L/h) was used as a starting value for streptomycin clearance, and volume was initially assumed to be 0.3 L/kg. Bayesian estimates of clearance were close to the starting value and declined from 1.4 L/h to 0.9 L/h during the course of therapy. Volume was higher than the initial estimate (0.4-0.5 L/kg), possibly due to the patient having a low albumin and being underweight. Satisfactory concentrations were maintained for several weeks with doses of 500 mg every 36-48 h.

This paper describes an adaptive control system for delivering heparin to maintain a desired level of anticoagulation during hemodialysis. Adaptive control was based on sparse measurements of the activated clotting time (ACT) measured in blood samples drawn from the extracorporeal circuit. A model-based control law was used in conjunction with Bayesian parameter estimation in the adaptive control system. The adaptive control system was validated in clinical trials during a routine 240-minute hemodialysis procedure. The mean of ACT values measured during adaptive control was 150 percent of the baseline ACT and the standard deviation was 11 percent.

The pharmacokinetic software package MW/Pharm offers an interactive, user-friendly program which gives rapid answers in clinical practice. It comprises a database with pharmacokinetic parameters of 180 drugs, a medication history database, and procedures for an individual drug dosage regimen calculation. The included curve-fitting facilities allow estimation of pharmacokinetic parameters on the basis of medication history, taking into account a varying status of the patient with respect to body weight and kidney function, optionally using a Bayesian procedure. The module KinBes performs the evaluation of bioavailability studies, including various methods, and an extensive statistical evaluation of bioequivalence.

A new set of guidelines for the administration of gentamicin was developed by estimating steady-state peak and trough gentamicin concentrations for simulated patients with known weights and creatinine clearances. The most appropriate doses to achieve target peak concentrations of 5-10 mg/L and troughs of less than 2 mg/L were then tabulated. The performance of these new guidelines was assessed using data collected from 60 patients who had received gentamicin and had at least two serum concentration measurements. Individual estimates of clearance and volume of distribution were obtained using a Bayesian parameter estimation program and these estimates were used to predict the steady-state peak and trough concentrations that would arise from the new guidelines and five other previously published nomograms (Mawer, Chan, Hull-Sarubbi, Rule of Eight, and Dettli). The new guidelines, the Dettli nomogram, and the Hull-Sarubbi table achieved similar percentages (52-57%) of patients within the target ranges (5-10 mg/L for peak and less than 2 mg/L for trough), although 28% of patients had predicted peak concentrations below 5 mg/L with the new method compared to 15% with the other two. Only 38% of patients were within both ranges when the Mawer nomogram and the Rule of Eight methods were used. Since a large percentage of patients would have achieved concentrations outside of the target ranges no matter which nomogram was used, serum concentration monitoring is still recommended to confirm dose requirements.

BACKGROUND Dihydropyrimidine dehydrogenase (DPD) deficiency leads to life-threatening 5-Fluorouracil toxicity. We developed a 2-13C-uracil (13C-Ura) breath test (BT) to rapidly screen cancer patients. Deficient patients have low breath 13CO2 concentrations due to reduced catabolism of 13C-Ura to 13C-dihydrouracil (13C-DHU) and 13CO2. AIM Develop a pharmacokinetic (PK) model to simultaneously describe 13C-Ura and catabolite concentrations in plasma and breath from normal and DPD deficient subjects. METHODS Blood and breath samples were collected for 3h after oral 13C-Ura (6 mg/kg) administration to 19 normal and 10 DPD deficient subjects. Plasma 13C-Ura, 13C-DHU, and breath 13CO2 concentrations were quantified. ADAPT II simultaneously fitted parent, metabolite, and breath concentration-time data. Akaike's Information Criterion was used to select the model. RESULTS A one compartment linear absorption/elimination parent/metabolite model with cumulative breath13CO2 elimination described concentration-time data of 13C-Ura, 13C-DHU, and 13CO2. Significant differences in modeled PK parameters between normal and DPD deficient subjects were observed. CONCLUSION This is the first model to simultaneously describe orally administered 13C-Ura disposition in normal and DPD deficient subjects. Bayesian parameter estimation and simulation studies will be used to identify an optimal limited sampling strategy to detect DPD deficiency in patients who aren't candidates for BT screening. (See Table) PK Parameter Normal Subjects (Mean ± SD) DPD Deficient Subjects (Mean ± SD) Mann-Whitney U Test p Value 13C-Ura Half-life (h) 0.18 ± 0.07 0.60 ± 0.67 ≤ 0.001 13C-Ura Clearance (L/kg/h) 1.54 ± 0.63 0.77 ± 0.30 ≤ 0.001 13C-DHU Formation Rate Constant (1/h) 3.23 ± 2.48 1.24 ± 0.64 ≤ 0.001 Clinical Pharmacology & Therapeutics (2005) 79, P83–P83; doi: 10.1016/j.clpt.2005.12.298

The pharmacokinetics and dose requirements of disopyramide were evaluated in 10 infants and children during the course of routine therapeutic drug monitoring. Drug clearance was estimated using a Bayesian parameter estimation program and found to range from 0.09-0.31L/h/kg. Concentrations associated with a satisfactory response were achieved with doses of 15-20mg/kg/day in most patients, but some required higher doses of 25-30mg/kg/day.

To achieve enough variability for different performances, a number of new concert halls with secondary hard chambers coupled to the main floor have recently received positive evaluations. As a result, the acoustics of coupled spaces is drawing growing attention in architectural acoustics. In these coupled spaces, the sound energy decays with different exponential constants under certain conditions. For better understanding and control of the acoustics in coupled spaces an efficient measurement technique of identifying multiple decay times is therefore of critical importance. A Bayesian parameter estimation approach is applied to an algorithmic evaluation of decay times using measured Schroeders decay functions. This work discusses Bayesian analysis of systematic measurements in scaled‐down, coupled rooms and real coupled spaces.