Search

Expert judgement is a common method for software effort estimations in practice today. Estimators are often shown extra obsolete requirements together with the real ones to be implemented. Only one previous study has been conducted on if such practices bias the estimations. We conducted six experiments with both students and practitioners to study, and quantify, the effects of obsolete requirements on software estimation. By conducting a family of six experiments using both students and practitioners as research subjects ($$N=461$$ N = 461 ), and by using a Bayesian Data Analysis approach, we investigated different aspects of this effect. We also argue for, and show an example of, how we by using a Bayesian approach can be more confident in our results and enable further studies with small sample sizes. We found that the presence of obsolete requirements triggered an overestimation in effort across all experiments. The effect, however, was smaller in a field setting compared to using students as subjects. Still, the over-estimations triggered by the obsolete requirements were systematically around twice the percentage of the included obsolete ones, but with a large 95% credible interval. The results have implications for both research and practice in that the found systematic error should be accounted for in both studies on software estimation and, maybe more importantly, in estimation practices to avoid over-estimations due to this systematic error. We partly explain this error to be stemming from the cognitive bias of anchoring-and-adjustment, i.e. the obsolete requirements anchored a much larger software. However, further studies are needed in order to accurately predict this effect.

The Tafel slope is a key parameter often quoted to characterize the efficacy of an electrochemical catalyst. In this paper, we develop a Bayesian data analysis approach to estimate the Tafel slope from experimentally-measured current-voltage data. Our approach obviates the human intervention required by current literature practice for Tafel estimation, and provides robust, distributional uncertainty estimates. Using synthetic data, we illustrate how data insufficiency can unknowingly influence current fitting approaches, and how our approach allays these concerns. We apply our approach to conduct a comprehensive re-analysis of data from the CO2 reduction literature. This analysis reveals no systematic preference for Tafel slopes to cluster around certain "cardinal values” (e.g. 60 or 120 mV/decade). We hypothesize several plausible physical explanations for this observation, and discuss the implications of our finding for mechanistic analysis in electrochemical kinetic investigations. The Tafel slope in electrochemical catalysis is usually determined from experimental data and remains error-prone. Here, the authors develop a Bayesian approach for Tafel slope quantification, and apply it to study the prevalence of certain "cardinal" Tafel slopes in the electrochemical CO2 reduction literature.

Background. Nonlinear nonstationary processes that are available in various spheres of human activity are characterized by numerous uncertainties, fuzziness, incompleteness and low precision data. To perform forecasting of such processes it is necessary to carry out correctly the data processing that is why the problem of development and practical use of the new processing methods is very urgent. The methods should provide a possibility for performing high quality input data processing aiming to quality modeling and forecasting the processes under study. Objective. A short review of the Bayesian data analysis methods will be provided and an original methodology for identification and taking into consideration of possible uncertainties that are available in the problems of modeling and forecasting developed. And a new combined probabilistic and statistical model will be proposed for modeling and forecasting nonlinear nonstationary processes. Methods. A combined implementation methodology has been proposed that includes the following: Bayesian data processing technics, an optimal filter for preliminary data processing, linear and nonlinear regression for formal description and forecasting conditional variance and probabilistic model in the form of Bayesian network to forecast nonlinear nonstationary processes. Results. The proposed modeling method was tested on the problem of forecast estimation using the financial market data. The statistical data hired describe evolution of stock prices for well-known company. The computational experiments performed showed that quality of the short-term forecasts for volatility and the nonlinear nonstationary financial process itself are improved substantially thanks to the optimal filtering procedure and rational model structure selection. Application of the complex model that uses Bayesian network provided a possibility for improvement of probabilistic forecasts used for performing trade operations at the stock market. Conclusions. Forecasts estimation for nonlinear nonstationary processes is an urgent problem that can be solved in various ways. The proposed probabilistic and statistical method for estimating probabilistic forecasts used for performing trade operations at the stock market generated high quality results and will be extended and improved in the future.

A shortcoming of the typical use of the frequentist probabilistic approach in reliability analysis of limit state equations that appear in the geotechnical literature is that estimates of the uncertainty in reliability index (probability of failure) are not made. A Bayesian approach is demonstrated in the paper to overcome this shortcoming using the example of the pullout limit state for internal stability of mechanically stabilised earth (MSE) walls. The paper shows that in the Bayesian context, reliability index or probability of failure are modelled as probability distributions, and therefore readily incorporate the uncertainty in their estimates. The general approach provides a tool to communicate the level of risk to stakeholders that follows from the choice of design method and guidance to reduce uncertainty on the estimate of reliability. For practical purposes, the predictive reliability index may be used as an alternative measure of margin of safety that takes this uncertainty into account.

We introduce the new package dmbc that implements a Bayesian algorithm for clustering a set of binary dissimilarity matrices within a model-based framework. Specifically, we consider the case when S matrices are available, each describing the dissimilarities among the same n objects, possibly expressed by S subjects (judges), or measured under different experimental conditions, or with reference to different characteristics of the objects themselves. In particular, we focus on binary dissimilarities, taking values 0 or 1 depending on whether or not two objects are deemed as dissimilar. We are interested in analyzing such data using multidimensional scaling (MDS). Differently from standard MDS algorithms, our goal is to cluster the dissimilarity matrices and, simultaneously, to extract an MDS configuration specific for each cluster. To this end, we develop a fully Bayesian three-way MDS approach, where the elements of each dissimilarity matrix are modeled as a mixture of Bernoulli random vectors. The parameter estimates and the MDS configurations are derived using a hybrid Metropolis-Gibbs Markov Chain Monte Carlo algorithm. We also propose a BIC-like criterion for jointly selecting the optimal number of clusters and latent space dimensions. We illustrate our approach referring both to synthetic data and to a publicly available data set taken from the literature. For the sake of efficiency, the core computations in the package are implemented in C/C++. The package also allows the simulation of multiple chains through the support of the parallel package.

We present an introduction to some concepts of Bayesian data analysis in the context of atomic physics. Starting from basic rules of probability, we present the Bayes’ theorem and its applications. In particular we discuss about the limits of classic statistics methods and in which cases a Bayesian analysis is mandatory. Moreover, we show how probability values can be assigned to different possible models (different numbers of peaks, type peak profile, etc.) from the analysis of experimental data.

In this paper, variational inference is studied on manifolds with certain metrics. To solve the problem, the analysis is first proposed for the variational Bayesian on Lie group, and then extended to the manifold that is approximated by Lie groups. Then the convergence of the proposed algorithm with respect to the manifold metric is proved in two iterative processes: variational Bayesian expectation (VB-E) step and variational Bayesian maximum (VB-M) step. Moreover, the effective of different metrics for Bayesian analysis is discussed.

This paper employs Bayesian probability theory for analyzing data generated in femtosecond pump-probe photoelectron-photoion coincidence (PEPICO) experiments. These experiments allow investigating ultrafast dynamical processes in photoexcited molecules. Bayesian probability theory is consistently applied to data analysis problems occurring in these types of experiments such as background subtraction and false coincidences. We previously demonstrated that the Bayesian formalism has many advantages, amongst which are compensation of false coincidences, no overestimation of pump-only contributions, significantly increased signal-to-noise ratio, and applicability to any experimental situation and noise statistics. Most importantly, by accounting for false coincidences, our approach allows running experiments at higher ionization rates, resulting in an appreciable reduction of data acquisition times. In addition to our previous paper, we include fluctuating laser intensities, of which the straightforward implementation highlights yet another advantage of the Bayesian formalism. Our method is thoroughly scrutinized by challenging mock data, where we find a minor impact of laser fluctuations on false coincidences, yet a noteworthy influence on background subtraction. We apply our algorithm to data obtained in experiments and discuss the impact of laser fluctuations on the data analysis.

Engineers perform fatigue assessments to support structural integrity management. Given that the purpose of these calculations is linked to problems of decision making under various sources of uncertainty, probabilistic methods are often more useful than deterministic alternatives. Guidance on the direct probabilistic application of procedures in existing industrial standards is currently limited and dependencies between marginal probabilistic models are generally not considered, despite their potential significance being acknowledged. This paper proposes the use of Bayesian data analysis as a flexible and intuitive approach to coherently and consistently account for uncertainty and dependency in fatigue crack growth rate models. Various Bayesian models are established and presented, based on the same data as the existing models in BS 7910 (a widely used industrial standard). The models are compared in terms of their out of sample predictive accuracy, using methods with a basis in information theory and cross-validation. The Bayesian models exhibit an improved performance, with the most accurate predictions resulting from multi-level (hierarchical) models, which account for variation between constituent test datasets and partially pool information.

Recent technological advances have provided new settings to enhance individual-based data collection and computerized-tracking data have became common in many behavioral and social research. By adopting instantaneous tracking devices such as computer-mouse, wii, and joysticks, such data provide new insights for analysing the dynamic unfolding of response process. ssMousetrack is a R package for modeling and analysing computerized-tracking data by means of a Bayesian state-space approach. The package provides a set of functions to prepare data, fit the model, and assess results via simple diagnostic checks. This paper describes the package and illustrates how it can be used to model and analyse computerized-tracking data. A case study is also included to show the use of the package in empirical case studies.

Objectives The objective of this systematic review is to investigate the use of Bayesian data analysis in epidemiology in the past decade and particularly to evaluate the quality of research papers reporting the results of these analyses. Study Design and Setting Complete volumes of five major epidemiological journals in the period 2005–2015 were searched via PubMed. In addition, we performed an extensive within-manuscript search using a specialized Java application. Details of reporting on Bayesian statistics were examined in the original research papers with primary Bayesian data analyses. Results The number of studies in which Bayesian techniques were used for primary data analysis remains constant over the years. Though many authors presented thorough descriptions of the analyses they performed and the results they obtained, several reports presented incomplete method sections and even some incomplete result sections. Especially, information on the process of prior elicitation, specification, and evaluation was often lacking. Conclusion Though available guidance papers concerned with reporting of Bayesian analyses emphasize the importance of transparent prior specification, the results obtained in this systematic review show that these guidance papers are often not used. Additional efforts should be made to increase the awareness of the existence and importance of these checklists to overcome the controversy with respect to the use of Bayesian techniques. The reporting quality in epidemiological literature could be improved by updating existing guidelines on the reporting of frequentist analyses to address issues that are important for Bayesian data analyses.

Markov Chain Monte Carlo based Bayesian data analysis has now become the method of choice for analyzing and interpreting data in almost all disciplines of science. In astronomy, over the last decade, we have also seen a steady increase in the number of papers that employ Monte Carlo based Bayesian analysis. New, efficient Monte Carlo based methods are continuously being developed and explored. In this review, we first explain the basics of Bayesian theory and discuss how to set up data analysis problems within this framework. Next, we provide an overview of various Monte Carlo based methods for performing Bayesian data analysis. Finally, we discuss advanced ideas that enable us to tackle complex problems and thus hold great promise for the future. We also distribute downloadable computer software (available at https://github.com/sanjibs/bmcmc/ ) that implements some of the algorithms and examples discussed here., Comment: 49 pages, draft version, to appear in Annual Review of Astronomy and Astrophysics

This paper considers sampling in proportion to size from a partly unknown distribution. The applied context is the exploration for undiscovered resources, like oil accumulations in different deposits, where the most promising deposits are likely to be drilled first, based on some geologic size indicators (“creaming”). A Log-normal size model with exponentially decaying creaming factor turns out to have nice analytical features in this context, and fits well available data, as demonstrated in Lillestol and Sinding-Larsen (2017). This paper is a Bayesian follow-up, which provides posterior parameter densities and predictive densities of future discoveries, in the case of uninformative prior distributions. The theory is applied to the prediction of remaining petroleum accumulations to be found on the mature part of the Norwegian Continental Shelf.

Following the critical review of Seaman et al. (2012), we reflect on what is presumably the most essential aspect of Bayesian statistics, namely the selection of a prior density. In some cases, Bayesian inference remains fairly stable under a large range of noninformative prior distributions. However, as discussed by \citet{Hd}, there may also be unintended consequences of a choice of a noninformative prior and, these authors consider this problem ignored in Bayesian studies. As they based their argumentation on four examples, we reassess these examples and their Bayesian processing via different prior choices. Our conclusion is to lower the degree of worry about the impact of the prior, exhibiting an overall stability of the posterior distributions. We thus consider that the warnings of Seaman et al. (2012), while commendable, do not jeopardize the use of most noninformative priors., 15 pages, 8 figures, 5 tables

Many comparative effectiveness research and patient-centered outcomes research studies will need to be observational for one or both of two reasons: first, randomized trials are expensive and time-consuming; and second, only observational studies can answer some research questions. It is generally recognized that there is a need to increase the scientific validity and efficiency of observational studies. Bayesian methods for the design and analysis of observational studies are scientifically valid and offer many advantages over frequentist methods, including, importantly, the ability to conduct comparative effectiveness research/patient-centered outcomes research more efficiently. Bayesian data analysis is being introduced into outcomes studies that we are conducting. Our purpose here is to describe our view of some of the advantages of Bayesian methods for observational studies and to illustrate both realized and potential advantages by describing studies we are conducting in which various Bayesian methods have been or could be implemented.

The subtraction of the X-ray background in a PIXE spectrum has been the subject of many investigations and different techniques have been developed. These techniques vary from filtering to fitting polynomial functions. The promising Bayesian Statistics technique has been used in this study to eliminate the background from the spectrum in a rigorous and self-consistent manner. We compare the results of the Bayesian background subtraction method to that obtained by stripping and the “rolling ball”; method.

A Bayesian data-analysis approach to data sets of maximum undercooling temperatures recorded in repeated melting-cooling cycles of high-purity samples is proposed. The crystallization phenomenon is described in terms of a nonhomogeneous Poisson process driven by a temperature-dependent sample nucleation rate $J(T)$. The method was extensively tested by computer simulations and applied to real data for undercooled liquid Ge. It proved to be particularly useful in the case of scarce data sets where the usage of binned data would degrade the available experimental information.

This work is a case study of applying Bayesian analysis, a statistical data method, in the design optimization of permanent test-bar mold. The permanent test-bar mold is used in casting foundry to examine the metal quality. Since the current standard test-bar mold suffers from shrinkage porosity which detracts from best properties, a modified design is recently proposed to improve the mechanical properties. In order to validate the new design, Bayesian data analysis method is utilized to analyze the experimental data from the two designs. The effects of the mold designs and casting process operational parameters on the mechanical properties of castings are compared. Main effect to the mechanical properties is identified based on the Bayesian analysis.Copyright © 2012 by ASME

Discussion of "Statistical Inference: The Big Picture" by R. E. Kass [arXiv:1106.2895], Comment: Published in at http://dx.doi.org/10.1214/11-STS337C the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

This chapter discusses mathematical methods for online automatic tuning. After formulating the abstract model of automatic tuning, we review the proposed method, which comprises several novel concepts of automatic tuning, such as online automatic tuning, Bayesian data analysis for quantitative treatments of uncertainties, Bayesian suboptimal sequential experimental design, asymptotic optimality, finite startup, and infinite dilution. Experimental results reveal that the Bayesian sequential experimental design has advantages over random sampling, although random sampling combined with an accurate cost function model can be as good as the Bayesian sequential experimental design.

This paper presents a fault diagnosis method for power transformer. Fault diagnosis plays an importance role in the efforts for transformer diagnosis to shift form “preventive maintenance” to “condition based maintenance” (CBM), and consequently to reduce the maintenance cost. Ever since its birth, numerous techniques have been researched in this field, each method however, has its own advantages and disadvantages. Fault diagnosis is a challenging problem because there are numerous fault situations that can possibly happen to a electrical transformer. Temperature is one of the most importance parameters for the diagnosis of electrical transformers fault situation. Based on the appearance information of temperature, a fault monitoring system can make diagnosis intelligently, therefore it can provide a rapid scientific treatment options for the field staff. For the problem of transformer fault diagnosis based on temperature information, we use Bayesian probability density theory method to explain the equipment fault diagnosis results. The maximum membership probability principle will be adapted to judge whether the equipment is malfunctioning. The results shows that when the working temperature is between 64∼72 °C, the diagnosis results without compensation is working on normal state, while the diagnosis results with compensation is general fault state. Therefore, the diagnosis results without compensation have large error with the actual situation. The proposed methodology in this paper was testified having important significance in improving the reliability and the information level of transformer operation.

Survival data analysis aims at collecting data on durations spent in a state by a sample of units, in order to analyse the process of transition to a different state. Survival analysis applied to social and economic phenomena typically relies upon data on transitions collected, for a sample of units, in one or more follow-up surveys. We explore the effect of misclassification of the transition indicator on parameter estimates in an appropriate statistical model for the duration spent in an origin state. Some empirical investigations about the bias induced when ignoring misclassification are reported, extending the model to include the possibility that the rate of misclassification can vary across units according to the value of some covariates. Finally it is shown how a Bayesian approach can lead to parameter estimates.

The evaluation of social programs is typically hampered by serious methodological problems. Adopting the approach of Bayesian simultaneous estimation proposed by Lindley and Smith (1972) and operationally developed by Jackson, Novick, and Thayer (1971), Novick, Lewis, and Jackson (1973), and Lewis, Wang, and Novick (1975), we demonstrate how some of these problems can be avoided or solved. To accomplish this, we extend the operational method to marginal m-group mean analysis for the unbalanced case. Data from the Emergency School Aid Act (ESAA) evaluation are used for illustration. Bayesian m-group analysis techniques are applied to data gathered from 17 school districts to evaluate the effectiveness of each district's compensatory education program.

Currently, causes of the middle Pleistocene transition (MPT) – the onset of large-amplitude glacial variability with 100 kyr time scale instead of regular 41 kyr cycles before – are a challenging puzzle in Paleoclimatology. Here we show how a Bayesian data analysis based on machine learning approaches can help to reveal the main mechanisms underlying the Pleistocene variability, which most likely explain proxy records and can be used for testing existing theories. We construct a Bayesian data-driven model from benthic δ18O records (LR04 stack) accounting for the main factors which may potentially impact climate of the Pleistocene: internal climate dynamics, gradual trends, variations of insolation, and millennial variability. In contrast to some theories, we uncover that under long-term trends in climate, the strong glacial cycles have appeared due to internal nonlinear oscillations induced by millennial noise. We find that while the orbital Milankovitch forcing does not matter for the MPT onset, the obliquity oscillation phase-locks the climate cycles through the meridional gradient of insolation.