28 results on '"Bayesian data analysis"'
Search Results
2. Is it possible to disregard obsolete requirements? a family of experiments in software effort estimation
- Author
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Richard Berntsson Svensson and Lucas Gren
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Programvaruteknik ,Computer science ,Errors ,Bayesian probability ,Small Sample Size ,02 engineering and technology ,01 natural sciences ,95% credible intervals ,Bayesian data analysis ,Field (computer science) ,Research subjects ,010104 statistics & probability ,Software ,Order (exchange) ,0202 electrical engineering, electronic engineering, information engineering ,Credible interval ,Family of experiments ,0101 mathematics ,Students ,Bayesian approaches ,Estimation ,Software effort estimation ,business.industry ,Software Engineering ,Estimator ,020207 software engineering ,Systematic error ,Industrial engineering ,Cognitive bias ,Expert judgement ,Bayesian networks ,Software estimation ,business ,Information Systems - Abstract
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.
- Published
- 2021
3. Digital Communication of Public Service Information and its Effect on Citizens’ Perception of Received Information
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Sarah M. L. Krøtel
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Digitization ,Government ,Public Administration ,business.industry ,media_common.quotation_subject ,05 social sciences ,ComputingMilieux_LEGALASPECTSOFCOMPUTING ,Public relations ,Survey experiment ,Bayesian data analysis ,government - citizen interaction ,0506 political science ,ComputerSystemsOrganization_MISCELLANEOUS ,Perception ,0502 economics and business ,050602 political science & public administration ,Public service ,Business ,survey experiment ,Business and International Management ,050203 business & management ,media_common - Abstract
This paper explores how the development of digital solutions for communication and daily interaction between government and its citizens influences citizens’ satisfaction, trust and perceived importance of the information received from government. It illuminates this effect by drawing on a survey-experimental design in a Danish research setting. With digitization of public services happening so quickly, it leaves the question of how this transformation is actually viewed by the citizens. The change in medium from traditional communication by standard mail to digital communication can be argued to have both positive and negative effects. Some citizens might find it easy to rely on digital communication, others might perceive digital solutions as a challenge and an obstacle when receiving essential information, which again might foster greater dissatisfaction. Results of Bayesian statistical analysis suggest that the digitization of communication form has little effect on citizens’ trust and satisfaction with the information received. Further, results do indicate that the perceived importance of the information received is lower for information received digitally.
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- 2019
4. Bayesian Data Analysis for Software Engineering
- Author
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Carlo A. Furia, Robert Feldt, and Richard Torkar
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Soundness ,Computer science ,business.industry ,Bayesian data analysis ,Software engineering ,business - Abstract
Slowly but surely, statistical practices in the empirical sciences are undergoing a complete makeover. Researchers in empirical software engineering, where too statistics is an essential tool, must become familiar with these new practices to ensure rigor of their research methods and soundness of their research results.
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- 2021
5. Data Visualization and Analysis in Second Language Research
- Author
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Garcia, Guilherme
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Computer science ,multilevel models ,data analysis ,computer.software_genre ,Data type ,Bayesian data analysis ,Field (computer science) ,mixed-effects models ,ordinal regression ,Data visualization ,RStudio ,Corpus linguistics ,Quantitative research ,data visualization ,hierarchical models ,business.industry ,logistic regression ,Applied linguistics ,Statistical model ,second language research ,Second-language acquisition ,linear regression ,Artificial intelligence ,business ,computer ,Natural language processing - Abstract
This introduction to visualization techniques and statistical models for second language research focuses on three types of data (continuous, binary, and scalar), helping readers to understand regression models fully and to apply them in their work. Garcia offers advanced coverage of Bayesian analysis, simulated data, exercises, implementable script code, and practical guidance on the latest R software packages. The book, also demonstrating the benefits to the L2 field of this type of statistical work, is a resource for graduate students and researchers in second language acquisition, applied linguistics, and corpus linguistics who are interested in quantitative data analysis.
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- 2021
6. Effects of hyperbaric environment on endurance and metabolism are exposure time‐dependent in well‐trained mice
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Junichi Suzuki
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Time Factors ,Physiology ,NT‐PGC1α ,030204 cardiovascular system & hematology ,lcsh:Physiology ,chemistry.chemical_compound ,Mice ,0302 clinical medicine ,Citrate synthase ,Hypoxia ,lcsh:QP1-981 ,biology ,Pyruvate dehydrogenase complex ,Adaptation, Physiological ,medicine.anatomical_structure ,Original Article ,medicine.drug ,medicine.medical_specialty ,left ventricle ,03 medical and health sciences ,Gastrocnemius muscle ,Endurance training ,Physiology (medical) ,Internal medicine ,Lactate dehydrogenase ,Physical Conditioning, Animal ,medicine ,bayesian data analysis ,Animals ,Carnitine ,hybrid exercise ,skeletal muscle ,Muscle, Skeletal ,hyperbaric exposure ,Fatty acid metabolism ,business.industry ,Skeletal muscle ,Metabolism ,Original Articles ,Lipid Metabolism ,Mitochondria, Muscle ,Endocrinology ,chemistry ,Ventricle ,biology.protein ,Physical Endurance ,NT-PGC1 alpha ,Plantaris muscle ,business ,Energy Metabolism ,030217 neurology & neurosurgery - Abstract
Hyperbaric exposure (1.3 atmospheres absolute with 20.9% O2) for 1 h a day was shown to improve exercise capacity. The present study was designed to reveal whether the daily exposure time affects exercise performance and metabolism in skeletal and cardiac muscles. Male mice in the training group were housed in a cage with a wheel activity device for 7 weeks from 5 weeks old. Trained mice were then subjected to hybrid training (HT, endurance exercise for 30 min followed by sprint interval exercise for 30 min). Hyperbaric exposure was applied following daily HT for 15 min (15HT), 30 min (30HT), or 60 min (60HT) for 4 weeks. In the endurance capacity test, maximal work values were significantly increased by 30HT and 60HT. In the left ventricle (LV), activity levels of 3‐hydroxyacyl‐CoA‐dehydrogenase, citrate synthase, and carnitine palmitoyl transferase (CPT) 2 were significantly increased by 60HT. CPT2 activity levels were markedly increased by hyperbaric exposure in red gastrocnemius (Gr) and plantaris muscle (PL). Pyruvate dehydrogenase complex activity values in PL were enhanced more by 30HT and 60HT than by HT. Protein levels of N‐terminal isoform of PGC1α (NT‐PGC1α) protein were significantly enhanced in three hyperbaric exposed groups in Gr, but not in LV. These results indicate that hyperbaric exposure for 30 min or longer has beneficial effects on endurance, and 60‐min exposure has the potential to further increase performance by facilitating fatty acid metabolism in skeletal and cardiac muscles in highly trained mice. NT‐PGC1α may have important roles for these adaptations in skeletal muscle., Effects of the duration of daily hyperbaric exposure on metabolic enzyme activity levels for fatty acid oxidation
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- 2021
7. Digital Pathology During the COVID-19 Outbreak in Italy: Survey Study
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L. Terracciano, Daoud Rahal, Vincenzo Belsito, Abubaker Elamin, Sofia Manara, Paola Bossi, Simone Giaretto, Camilla De Carlo, Salvatore Lorenzo Renne, Angelo Cagini, Barbara Fiamengo, Bethania Fernandes, Cesare Lancellotti, Tatiana Brambilla, Miriam Cieri, Mauro Sollai, Stefania Rao, Paola Spaggiari, Marina Valeri, Massimo Roncalli, Piergiuseppe Colombo, and Luca Di Tommaso
- Subjects
Diagnostic Imaging ,medicine.medical_specialty ,Coronavirus disease 2019 (COVID-19) ,COVID19 ,Health Informatics ,Audiology ,probabilistic modeling ,Affect (psychology) ,lcsh:Computer applications to medicine. Medical informatics ,01 natural sciences ,Bayesian data analysis ,Disease Outbreaks ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,Surveys and Questionnaires ,Epidemiology ,Image Processing, Computer-Assisted ,Humans ,Medicine ,0101 mathematics ,Medical diagnosis ,Microscopy ,Original Paper ,Pathology, Clinical ,business.industry ,lcsh:Public aspects of medicine ,COVID-19 ,Internship and Residency ,Outbreak ,Digital pathology ,Bayes Theorem ,lcsh:RA1-1270 ,Gold standard (test) ,Italy ,030220 oncology & carcinogenesis ,lcsh:R858-859.7 ,Clinical Competence ,digital pathology ,business - Abstract
Background Transition to digital pathology usually takes months or years to be completed. We were familiarizing ourselves with digital pathology solutions at the time when the COVID-19 outbreak forced us to embark on an abrupt transition to digital pathology. Objective The aim of this study was to quantitatively describe how the abrupt transition to digital pathology might affect the quality of diagnoses, model possible causes by probabilistic modeling, and qualitatively gauge the perception of this abrupt transition. Methods A total of 17 pathologists and residents participated in this study; these participants reviewed 25 additional test cases from the archives and completed a final psychologic survey. For each case, participants performed several different diagnostic tasks, and their results were recorded and compared with the original diagnoses performed using the gold standard method (ie, conventional microscopy). We performed Bayesian data analysis with probabilistic modeling. Results The overall analysis, comprising 1345 different items, resulted in a 9% (117/1345) error rate in using digital slides. The task of differentiating a neoplastic process from a nonneoplastic one accounted for an error rate of 10.7% (42/392), whereas the distinction of a malignant process from a benign one accounted for an error rate of 4.2% (11/258). Apart from residents, senior pathologists generated most discrepancies (7.9%, 13/164). Our model showed that these differences among career levels persisted even after adjusting for other factors. Conclusions Our findings are in line with previous findings, emphasizing that the duration of transition (ie, lengthy or abrupt) might not influence the diagnostic performance. Moreover, our findings highlight that senior pathologists may be limited by a digital gap, which may negatively affect their performance with digital pathology. These results can guide the process of digital transition in the field of pathology.
- Published
- 2021
8. Metastatic Breast Cancer and Pre-Diagnostic Blood Gene Expression Profiles—The Norwegian Women and Cancer (NOWAC) Post-Genome Cohort
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Karina Standahl Olsen and Einar Holsbø
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0301 basic medicine ,Oncology ,medicine.medical_specialty ,Cancer Research ,Disease ,lcsh:RC254-282 ,Bayesian data analysis ,causal diagrams ,Metastasis ,03 medical and health sciences ,transcriptomics ,0302 clinical medicine ,Breast cancer ,breast cancer ,blood ,Internal medicine ,medicine ,metastasis ,Gene ,Whole blood ,Original Research ,VDP::Medisinske Fag: 700::Helsefag: 800::Samfunnsmedisin, sosialmedisin: 801 ,business.industry ,Absolute risk reduction ,Cancer ,medicine.disease ,lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,Metastatic breast cancer ,immune system ,030104 developmental biology ,030220 oncology & carcinogenesis ,VDP::Medical disciplines: 700::Health sciences: 800::Community medicine, Social medicine: 801 ,business - Abstract
Breast cancer patients with metastatic disease have a higher incidence of deaths from breast cancer than patients with early-stage cancers. Recent findings suggest that there are differences in immune cell function between metastatic and non-metastatic cases, even years before diagnosis. We have analyzed whole blood gene expression by Illumina bead chips in blood samples taken using the PAXgene blood collection system up to two years before diagnosis. The final study sample included 197 breast cancer cases and 197 age-matched controls. We defined a causal directed acyclic graph to guide a Bayesian data analysis to estimate the risk of metastasis associated with the expression of all genes and with relevant sets of genes. We ranked genes and gene sets according to the sign probability for excess risk. Among the screening detected cancers, 82% were without metastasis, compared to 53% of between-screening detected cancers. Among the highest ranking genes and gene sets associated with metastasis risk, we identified plasmacytiod dentritic cell function, the SLC22 family of transporters, and glutamine metabolism as potential links between the immune system and metastasis. We conclude that there may be potentially wide-reaching differences in blood gene expression profiles between metastatic and non-metastatic breast cancer cases up to two years before diagnosis, which warrants future study.
- Published
- 2020
9. Statistical Models for the Analysis of Optimization Algorithms with Benchmark Functions
- Author
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David Issa Mattos, Jan Bosch, and Helena Holmström Olsson
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FOS: Computer and information sciences ,Optimization algorithm ,Computer science ,business.industry ,Statistical model ,Bayesian data analysis ,Machine learning ,computer.software_genre ,Evolutionary computation ,Theoretical Computer Science ,Methodology (stat.ME) ,Computational Theory and Mathematics ,Frequentist inference ,Benchmark (computing) ,Code (cryptography) ,Artificial intelligence ,business ,computer ,Software ,Statistics - Methodology ,Statistical hypothesis testing - Abstract
Frequentist statistical methods, such as hypothesis testing, are standard practices in studies that provide benchmark comparisons. Unfortunately, these methods have often been misused, e.g., without testing for their statistical test assumptions or without controlling for family-wise errors in multiple group comparisons, among several other problems. Bayesian Data Analysis (BDA) addresses many of the previously mentioned shortcomings but its use is not widely spread in the analysis of empirical data in the evolutionary computing community. This paper provides three main contributions. First, we motivate the need for utilizing Bayesian data analysis and provide an overview of this topic. Second, we discuss the practical aspects of BDA to ensure that our models are valid and the results are transparent. Finally, we provide five statistical models that can be used to answer multiple research questions. The online appendix provides a step-by-step guide on how to perform the analysis of the models discussed in this paper, including the code for the statistical models, the data transformations, and the discussed tables and figures.
- Published
- 2020
10. True malaria prevalence in children under five: Bayesian estimation using data of malaria household surveys from three sub-Saharan countries
- Author
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Carine Van Malderen, Dejan Zurovac, Dieter Vanderelst, Elvire Mfueni, Léon Tshilolo, Angel Rosas-Aguirre, Bernhards Ogutu, Brecht Devleesschauwer, Robert W. Snow, Niko Speybroeck, and Patrick T. Brandt
- Subjects
AFRICA ,medicine.medical_specialty ,lcsh:Arctic medicine. Tropical medicine ,lcsh:RC955-962 ,030231 tropical medicine ,Prevalence ,DISEASE PREVALENCE ,True prevalence ,RAPID DIAGNOSTIC-TEST ,Bayesian data analysis ,lcsh:Infectious and parasitic diseases ,03 medical and health sciences ,0302 clinical medicine ,Environmental health ,parasitic diseases ,medicine ,Medicine and Health Sciences ,Humans ,lcsh:RC109-216 ,030212 general & internal medicine ,Africa South of the Sahara ,Retrospective Studies ,Estimation ,Rapid diagnostic test ,Conditional dependence ,Under-five ,PLASMODIUM-FALCIPARUM ,Sub-Saharan Africa ,business.industry ,Public health ,Research ,Infant ,Bayes Theorem ,MICROSCOPY ,Gold standard (test) ,GOLD STANDARD ,medicine.disease ,3. Good health ,Malaria ,Infectious Diseases ,Mathematics and Statistics ,PCR ,Child, Preschool ,TESTS ,Parasitology ,business ,KENYA - Abstract
Background Malaria is one of the major causes of childhood death in sub-Saharan countries. A reliable estimation of malaria prevalence is important to guide and monitor progress toward control and elimination. The aim of the study was to estimate the true prevalence of malaria in children under five in the Democratic Republic of the Congo, Uganda and Kenya, using a Bayesian modelling framework that combined in a novel way malaria data from national household surveys with external information about the sensitivity and specificity of the malaria diagnostic methods used in those surveys—i.e., rapid diagnostic tests and light microscopy. Methods Data were used from the Demographic and Health Surveys (DHS) and Malaria Indicator Surveys (MIS) conducted in the Democratic Republic of the Congo (DHS 2013–2014), Uganda (MIS 2014–2015) and Kenya (MIS 2015), where information on infection status using rapid diagnostic tests and/or light microscopy was available for 13,573 children. True prevalence was estimated using a Bayesian model that accounted for the conditional dependence between the two diagnostic methods, and the uncertainty of their sensitivities and specificities obtained from expert opinion. Results The estimated true malaria prevalence was 20% (95% uncertainty interval [UI] 17%–23%) in the Democratic Republic of the Congo, 22% (95% UI 9–32%) in Uganda and 1% (95% UI 0–3%) in Kenya. According to the model estimations, rapid diagnostic tests had a satisfactory sensitivity and specificity, and light microscopy had a variable sensitivity, but a satisfactory specificity. Adding reported history of fever in the previous 14 days as a third diagnostic method to the model did not affect model estimates, highlighting the poor performance of this indicator as a malaria diagnostic. Conclusions In the absence of a gold standard test, Bayesian models can assist in the optimal estimation of the malaria burden, using individual results from several tests and expert opinion about the performance of those tests. Electronic supplementary material The online version of this article (10.1186/s12936-018-2211-y) contains supplementary material, which is available to authorized users.
- Published
- 2018
11. An Introduction to Bayesian Data Analysis for Correlations
- Author
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Regina Nuzzo
- Subjects
Biomedical Research ,business.industry ,Statistics as Topic ,05 social sciences ,Rehabilitation ,Bayes Theorem ,Physical Therapy, Sports Therapy and Rehabilitation ,Bayesian data analysis ,Machine learning ,computer.software_genre ,050105 experimental psychology ,03 medical and health sciences ,0302 clinical medicine ,Neurology ,Humans ,Medicine ,0501 psychology and cognitive sciences ,Neurology (clinical) ,Artificial intelligence ,business ,computer ,030217 neurology & neurosurgery - Published
- 2017
12. Bayesian Data Analysis for Animal Scientists: The Basics
- Author
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Kevin McConway
- Subjects
Statistics and Probability ,Economics and Econometrics ,business.industry ,Artificial intelligence ,Sociology ,Statistics, Probability and Uncertainty ,business ,Bayesian data analysis ,Social Sciences (miscellaneous) - Published
- 2019
13. Correction to: Bayesian Data Analysis for Animal Scientists
- Author
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Agustín Blasco
- Subjects
business.industry ,Computer science ,Artificial intelligence ,business ,Bayesian data analysis ,Machine learning ,computer.software_genre ,computer - Published
- 2018
14. Reporting of Bayesian analysis in epidemiologic research should become more transparent
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Karel G.M. Moons, Irene Klugkist, Charlotte Rietbergen, Kristel J.M. Janssen, and Thomas P. A. Debray
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Research Report ,Epidemiology ,Process (engineering) ,Reporting quality ,media_common.quotation_subject ,Bayesian probability ,030209 endocrinology & metabolism ,Review ,Bayesian statistics ,Original research ,03 medical and health sciences ,0302 clinical medicine ,Frequentist inference ,Journal Article ,Medicine ,Humans ,Quality (business) ,030212 general & internal medicine ,Epidemiologic research ,media_common ,Management science ,business.industry ,Bayes Theorem ,Reporting guidelines ,Bayesian data analysis ,Data science ,Epidemiologic Studies ,Epidemiologic Research Design ,Systematic review ,business - Abstract
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.
- Published
- 2017
15. Creaming - and the Depletion of Resources: A Bayesian Data Analysis
- Author
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Jostein Lillestøl and Richard Sinding-Larsen
- Subjects
Norwegian continental shelf ,Engineering ,business.industry ,Bayesian probability ,Sampling (statistics) ,Context (language use) ,Soil science ,Bayesian data analysis ,chemistry.chemical_compound ,Creaming ,chemistry ,Statistics ,Log-normal distribution ,Petroleum ,business - Abstract
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.
- Published
- 2017
16. The return on investment for taxi companies transitioning to electric vehicles
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Andrew R. Curtis, Srinivasan Keshav, and Tommy Carpenter
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Finance ,business.industry ,Taxis ,Transportation ,Development ,Bayesian data analysis ,Corporation ,Profit (economics) ,Transport engineering ,Return on investment ,Public transport ,Revenue ,Electricity ,business ,Civil and Structural Engineering - Abstract
We study whether taxi companies can simultaneously save petroleum and money by transitioning to electric vehicles. We propose a process to compute the return on investment of transitioning a taxi corporation’s fleet to electric vehicles. We use Bayesian data analysis to infer the revenue changes associated with the transition. We do not make any assumptions about the vehicles’ mobility patterns; instead, we use a time-series of GPS coordinates of the company’s existing petroleum-based vehicles to derive our conclusions. As a case study, we apply our process to a major taxi corporation, Yellow Cab San Francisco (YCSF). Using current prices, we find that transitioning their fleet to battery electric vehicles and plug-in hybrid electric vehicles is profitable for the company. Furthermore, given that gasoline prices in San Francisco are only 5.4 % higher than the rest of the United States, but electricity prices are 75 % higher; taxi companies with similar practices and mobility patterns in other cities are likely to profit more than YCSF by transitioning to electric vehicles.
- Published
- 2013
17. A Primer in Bayesian Data Analysis
- Author
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James R. Thompson
- Subjects
symbols.namesake ,business.industry ,Expectation–maximization algorithm ,symbols ,Pattern recognition ,Artificial intelligence ,business ,Bayesian data analysis ,Primer (cosmetics) ,Gibbs sampling ,Mathematics - Published
- 2011
18. Approximate Bayesian Computation (ABC) in practice
- Author
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Oscar E. Gaggiotti, Katalin Csilléry, Michael G. B. Blum, Olivier François, TIMB, Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble - UMR 5525 (TIMC-IMAG), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF), Laboratoire d'Ecologie Alpine (LECA), and Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])
- Subjects
0106 biological sciences ,MESH: Bayes Theorem ,Ecology (disciplines) ,MESH: Algorithms ,MESH: Biological Evolution ,Biostatistics ,MESH: Africa ,MESH: Biostatistics ,Biology ,Natural variation ,Machine learning ,computer.software_genre ,010603 evolutionary biology ,01 natural sciences ,Article ,MESH: Biodiversity ,MESH: Drosophila melanogaster ,03 medical and health sciences ,Bayes' theorem ,MESH: Demography ,Animals ,MESH: Animals ,MESH: Models, Genetic ,Ecology, Evolution, Behavior and Systematics ,Demography ,030304 developmental biology ,0303 health sciences ,[SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE] ,Models, Genetic ,business.industry ,Bayes Theorem ,Biodiversity ,Bayesian data analysis ,Biological Evolution ,Drosophila melanogaster ,Animal ecology ,Africa ,Evolutionary ecology ,Artificial intelligence ,Approximate Bayesian computation ,business ,computer ,Algorithms ,Simulation methods - Abstract
International audience; Understanding the forces that influence natural variation within and among populations has been a major objective of evolutionary biologists for decades. Motivated by the growth in computational power and data complexity, modern approaches to this question make intensive use of simulation methods. Approximate Bayesian Computation (ABC) is one of these methods. Here we review the foundations of ABC, its recent algorithmic developments, and its applications in evolutionary biology and ecology. We argue that the use of ABC should incorporate all aspects of Bayesian data analysis: formulation, fitting, and improvement of a model. ABC can be a powerful tool to make inferences with complex models if these principles are carefully applied.
- Published
- 2010
19. Kruschke, J. K. (2011). Doing Bayesian Data Analysis: A Tutorial with R and BUGS. Burlington, MA: Academic Press
- Author
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Kimberly F. Colvin
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Computer science ,business.industry ,Developmental and Educational Psychology ,Psychology (miscellaneous) ,Artificial intelligence ,Bayesian data analysis ,business ,Applied Psychology ,Education - Published
- 2013
20. Bayesian data analysis in observational comparative effectiveness research: rationale and examples
- Author
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Lian Mao, William H. Olson, Scott M. Lynch, Concetta Crivera, Jessica Panish, and Yi-Wen Ma
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medicine.medical_specialty ,Comparative Effectiveness Research ,Comparative effectiveness research ,Bayesian probability ,law.invention ,Randomized controlled trial ,Frequentist inference ,law ,Patient-Centered Care ,Econometrics ,medicine ,Humans ,Prospective Studies ,Acute Coronary Syndrome ,Retrospective Studies ,Management science ,business.industry ,Health Policy ,Bayes Theorem ,Observational methods in psychology ,Bayesian data analysis ,Patient Outcome Assessment ,Observational Studies as Topic ,Research Design ,Observational study ,Outcomes research ,business ,Delivery of Health Care - Abstract
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.
- Published
- 2013
21. Bayesian PIXE background subtraction
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M. S. Nkwinika, J. Padayachee, W. von der Linden, V. M. Prozesky, and Volker Dose
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Physics ,Nuclear and High Energy Physics ,Background subtraction ,Polynomial ,business.industry ,Bayesian probability ,Subtraction ,Analytical chemistry ,Pattern recognition ,Bayesian data analysis ,Bayesian statistics ,Artificial intelligence ,business ,Instrumentation - Abstract
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.
- Published
- 1999
22. Application of Bayesian Analysis Method in the Design Optimization of Permanent Casting Mold
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Yaou Wang and David Schwam
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Engineering ,Engineering drawing ,Casting mold ,business.industry ,Bayesian probability ,Experimental data ,Mechanical engineering ,medicine.disease_cause ,Bayesian data analysis ,Casting ,Mold ,medicine ,Main effect ,Foundry ,business - Abstract
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
- Published
- 2012
23. Bayesian Data Analysis
- Author
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Michael S. Wheatland
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Bayesian statistics ,Computer science ,business.industry ,Bayesian hierarchical modeling ,Bayes factor ,Artificial intelligence ,Bayesian linear regression ,Machine learning ,computer.software_genre ,business ,Bayesian data analysis ,computer - Published
- 2010
24. Chapter 5 Highlighting
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John K. Kruschke
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Computer science ,business.industry ,education ,Posterior probability ,Base (topology) ,Machine learning ,computer.software_genre ,Bayesian data analysis ,Learning theory ,Artificial intelligence ,Differential (infinitesimal) ,business ,computer ,Web site - Abstract
Highlighting is a perplexing effect in learning, in which shared features are more strongly associated with early learned outcomes but distinctive features are more strongly associated with later learned outcomes. The effect has been widely observed with different stimuli, procedures, and application domains. It continues to discomfit many theories of learning. This chapter provides results from a “canonical” design in which the base rates of early and late outcomes are equalized. This balanced design yields data that pose a challenge to models that have relied on differential base rates of past designs to mimic highlighting. The data are available at the author's Web site as a test bed for models. A Bayesian data analysis is also reported that provides explicit posterior distributions over choice probabilities. The posterior distribution is also available online.
- Published
- 2009
25. Aspects of the Combination of Evidence
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Colin Aitken, Franco Taroni, Alex Biedermann, and Paolo Garbolino
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Engineering ,business.industry ,Econometrics ,Bayesian data analysis ,business ,Data science - Published
- 2006
26. [Untitled]
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Daniel G. Goldstein
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Economics and Econometrics ,Sociology and Political Science ,business.industry ,Artificial intelligence ,Bayesian data analysis ,Psychology ,business ,Applied Psychology - Published
- 2011
27. Bayesian Data Analysis, 2nd edn
- Author
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Anders Brix
- Subjects
Statistics and Probability ,Economics and Econometrics ,business.industry ,Artificial intelligence ,Statistics, Probability and Uncertainty ,Bayesian data analysis ,business ,Machine learning ,computer.software_genre ,computer ,Social Sciences (miscellaneous) ,Mathematics - Published
- 2004
28. Bayesian Data Analysis
- Author
-
M. Elizabeth Halloran, Donald B. Rubin, Hal S. Stern, Thomas A. Louis, Andrew Gelman, John B. Carlin, and Bradley P. Carlin
- Subjects
Statistics and Probability ,Computer science ,business.industry ,Bayes factor ,Bayesian data analysis ,Machine learning ,computer.software_genre ,Bayesian statistics ,Bayes' theorem ,Bayesian hierarchical modeling ,Artificial intelligence ,Statistics, Probability and Uncertainty ,business ,computer - Published
- 1997
Catalog
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