Moving Beyond Non Informative Prior Distributions Achieving The Full Potential Of Bayesian Methods For Psychological Research
DOWNLOAD
Download Moving Beyond Non Informative Prior Distributions Achieving The Full Potential Of Bayesian Methods For Psychological Research PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Moving Beyond Non Informative Prior Distributions Achieving The Full Potential Of Bayesian Methods For Psychological Research book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Moving Beyond Non Informative Prior Distributions Achieving The Full Potential Of Bayesian Methods For Psychological Research
DOWNLOAD
Author : Christoph Koenig
language : en
Publisher: Frontiers Media SA
Release Date : 2022-02-01
Moving Beyond Non Informative Prior Distributions Achieving The Full Potential Of Bayesian Methods For Psychological Research written by Christoph Koenig and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-01 with Science categories.
Bayesian Structural Equation Modeling
DOWNLOAD
Author : Sarah Depaoli
language : en
Publisher: Guilford Publications
Release Date : 2021-08-16
Bayesian Structural Equation Modeling written by Sarah Depaoli and has been published by Guilford Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-16 with Social Science categories.
This book offers researchers a systematic and accessible introduction to using a Bayesian framework in structural equation modeling (SEM). Stand-alone chapters on each SEM model clearly explain the Bayesian form of the model and walk the reader through implementation. Engaging worked-through examples from diverse social science subfields illustrate the various modeling techniques, highlighting statistical or estimation problems that are likely to arise and describing potential solutions. For each model, instructions are provided for writing up findings for publication, including annotated sample data analysis plans and results sections. Other user-friendly features in every chapter include "Major Take-Home Points," notation glossaries, annotated suggestions for further reading, and sample code in both Mplus and R. The companion website (www.guilford.com/depaoli-materials) supplies data sets; annotated code for implementation in both Mplus and R, so that users can work within their preferred platform; and output for all of the book’s examples.
Novel Applications Of Bayesian And Other Models In Translational Neuroscience
DOWNLOAD
Author : Reza Rastmanesh
language : en
Publisher: Frontiers Media SA
Release Date : 2024-05-06
Novel Applications Of Bayesian And Other Models In Translational Neuroscience written by Reza Rastmanesh and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-06 with Science categories.
It has been proposed that the brain works in a Bayesian manner, and based on the free-energy principle, the brain's main function is to reduce environmental uncertainty; this is a proposed model as a universal principle governing adaptive brain function and structure. There are many pathophysiological, and clinical observations that can be easily explained by predictive Bayesian brain models. However, the novel applications of Bayesian models in translational neuroscience has been understudied and underreported. For example, variational Bayesian mixed-effects inference has been successfully tested for classification studies. A multi-task Bayesian compressive sensing approach to simultaneously estimate the full posterior of the CSA-ODF and diffusion-weighted volumes from multi-shell HARDI acquisitions has been recently publishe
Proceedings Of The British Psychological Society
DOWNLOAD
Author : British Psychological Society
language : en
Publisher:
Release Date : 1993
Proceedings Of The British Psychological Society written by British Psychological Society and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Psychology categories.
Noninformative Bayesian Priors For Large Samples Based On Shannon Information Theory
DOWNLOAD
Author : Stacy D. Hill
language : en
Publisher:
Release Date : 1987
Noninformative Bayesian Priors For Large Samples Based On Shannon Information Theory written by Stacy D. Hill and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1987 with categories.
We consider the problem of producing non-informative prior distributions for Bayesian analysis. The definition of non-informative adopted here is based on maximizing an intuitively appealing information measure derived from Shannon information theory. Based on large-sample (asymptotic) considerations, we show how the resulting generally intractable optimization problem can be significantly simplified. This differs from the authors' previous work on non-informative priors, which considered finite-samples and showed how a tractable suboptimal solution could be obtained. Reprints. (mjm).
Bayesian Methods
DOWNLOAD
Author : Jeff Gill
language : en
Publisher: CRC Press
Release Date : 2014-12-11
Bayesian Methods written by Jeff Gill and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-11 with Mathematics categories.
An Update of the Most Popular Graduate-Level Introductions to Bayesian Statistics for Social ScientistsNow that Bayesian modeling has become standard, MCMC is well understood and trusted, and computing power continues to increase, Bayesian Methods: A Social and Behavioral Sciences Approach, Third Edition focuses more on implementation details of th
Obtaining Accurate Estimates Of The Mediated Effect With And Without Prior Information
DOWNLOAD
Author : Milica Miocevic
language : en
Publisher:
Release Date : 2014
Obtaining Accurate Estimates Of The Mediated Effect With And Without Prior Information written by Milica Miocevic and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Bayesian statistical decision theory categories.
Research methods based on the frequentist philosophy use prior information in a priori power calculations and when determining the necessary sample size for the detection of an effect, but not in statistical analyses. Bayesian methods incorporate prior knowledge into the statistical analysis in the form of a prior distribution. When prior information about a relationship is available, the estimates obtained could differ drastically depending on the choice of Bayesian or frequentist method. Study 1 in this project compared the performance of five methods for obtaining interval estimates of the mediated effect in terms of coverage, Type I error rate, empirical power, interval imbalance, and interval width at N = 20, 40, 60, 100 and 500. In Study 1, Bayesian methods with informative prior distributions performed almost identically to Bayesian methods with diffuse prior distributions, and had more power than normal theory confidence limits, lower Type I error rates than the percentile bootstrap, and coverage, interval width, and imbalance comparable to normal theory, percentile bootstrap, and the bias-corrected bootstrap confidence limits. Study 2 evaluated if a Bayesian method with true parameter values as prior information outperforms the other methods. The findings indicate that with true values of parameters as the prior information, Bayesian credibility intervals with informative prior distributions have more power, less imbalance, and narrower intervals than Bayesian credibility intervals with diffuse prior distributions, normal theory, percentile bootstrap, and bias-corrected bootstrap confidence limits. Study 3 examined how much power increases when increasing the precision of the prior distribution by a factor of ten for either the action or the conceptual path in mediation analysis. Power generally increases with increases in precision but there are many sample size and parameter value combinations where precision increases by a factor of 10 do not lead to substantial increases in power.
Prior Processes And Their Applications
DOWNLOAD
Author : Eswar G. Phadia
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-07-25
Prior Processes And Their Applications written by Eswar G. Phadia and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-25 with Mathematics categories.
This book presents a systematic and comprehensive treatment of various prior processes that have been developed over the last four decades in order to deal with the Bayesian approach to solving some nonparametric inference problems. Applications of these priors in various estimation problems are presented. Starting with the famous Dirichlet process and its variants, the first part describes processes neutral to the right, gamma and extended gamma, beta and beta-Stacy, tail free and Polya tree, one and two parameter Poisson-Dirichlet, the Chinese Restaurant and Indian Buffet processes, etc., and discusses their interconnection. In addition, several new processes that have appeared in the literature in recent years and which are off-shoots of the Dirichlet process are described briefly. The second part contains the Bayesian solutions to certain estimation problems pertaining to the distribution function and its functional based on complete data. Because of the conjugacy property of some of these processes, the resulting solutions are mostly in closed form. The third part treats similar problems but based on right censored data. Other applications are also included. A comprehensive list of references is provided in order to help readers explore further on their own.
Bayesian Cognitive Modeling
DOWNLOAD
Author : Michael D. Lee
language : en
Publisher: Cambridge University Press
Release Date : 2014-04-03
Bayesian Cognitive Modeling written by Michael D. Lee and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-04-03 with Psychology categories.
Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS, and supported by Matlab and R), with additional support available online. No advance knowledge of statistics is required and, from the very start, readers are encouraged to apply and adjust Bayesian analyses by themselves. The book contains a series of chapters on parameter estimation and model selection, followed by detailed case studies from cognitive science. After working through this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.
Constrained Bayesian Inference For Density Estimation And Informative Bayesian Models
DOWNLOAD
Author : Jihyeon Lee
language : en
Publisher:
Release Date : 2022
Constrained Bayesian Inference For Density Estimation And Informative Bayesian Models written by Jihyeon Lee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Statistics categories.
When we construct a Bayesian hierarchical model, we are required to specify a prior distribution. There are some considerations when specifying the prior distribution, such as prior misspecification, and this use of preliminary estimates. This dissertation proposes to constrain a Bayesian model on a ball centering at a preliminary estimate or the ball's complement (id est, disc). This approach extends the constrained Bayesian model in Bradley and Zong (2021) to consider the ball or the disc in specific inferential settings. In Chpater 2, we are motivated to estimate the change in the distribution of housing sale prices in the Manhattan housing market during the COVID-19 pandemic with strict COVID-19 guidelines and social distancing policies using individual transaction and property data from Zillow (ZTRAX). To improve the precision of the Dirichlet process mixture model (DPM) for density estimation, we adopt a constrained Bayes approach incorporating the kernel density estimator (KDE). Specifically, this approach constrains the joint support of the data and parameters in the DPM on either a set centered around a KDE or the complement of this set. When the KDE is a reasonable (as measured by the Kolmogorov-Smirnov statistic) preliminary estimator, we constrain the DPM to be close to the KDE and vice versa. We call our method the density-constrained Bayesian hierarchical model (D-CBHM). By doing so, we can simultaneously account for all sources of variability as well as incorporate preliminary information from the KDE. We demonstrate that the D-CBHM analytically (under reasonable conditions) and empirically outperforms the DPM in mean integrated squared error. We apply our method to our motivating dataset of Manhattan's home sales price and estimate how the COVID-19 pandemic changed the market in volume and distribution. In Chapter 3, we introduce the constrained Bayesian hierarchical model (CBM), which considers Bayesian models with both a noninformative and an informative prior distribution. Specifically, we constrain the joint support of the data and parameters in a noninformative Bayesian model on a ball centering at an informative posterior estimate, such as the posterior mean in the Bayesian model with an informative prior distribution or its complement. This approach incorporates prior sensitivity analysis by considering two different prior distributions. It also alleviates potential concerns related to prior misspecification but still uses the given prior information outside the Bayesian model whose support is constrained. We then prove that the constrained model outperforms the Bayesian model solely with the noninformative and the informative prior distributions in terms of mean squared error. In practice, the set which the Bayesian model is constrained on is determined based on the Kolmogorov-Smirnov statistic and the posterior expected mean squared error, and the Bayesian model whose support is constrained is determined based on the predictive accuracy measure such as Watanabe-Akaike information criterion. We show that the practical CBM empirically and generally outperforms the (unconstrained) Bayesian model. We apply the practical CBM to Genotype-Tissue Expression (GTEx) data to estimate the association between gene expressions and single nucleotide polymorphisms (SNPs).