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Case Studies in Bayesian Statistics: Volume V

Posted By: AvaxGenius
Case Studies in Bayesian Statistics: Volume V

Case Studies in Bayesian Statistics: Volume V by Constantine Gatsonis, Robert E. Kass, Bradley Carlin, Alicia Carriquiry, Andrew Gelman, Isabella Verdinelli, Mike West
English | PDF | 2002 | 441 Pages | ISBN : 0387951695 | 38.1 MB

The 5th Workshop on Case Studies in Bayesian Statistics was held at the Carnegie Mellon University campus on September 24-25, 1999. As in the past, the workshop featured both invited and contributed case studies. The former were presented and discussed in detail while the latter were presented in poster format. This volume contains the three invited case studies with the accompanying discussion as well as ten contributed pa­ pers selected by a refereeing process. The majority of case studies in the volume come from biomedical research. However, the reader will also find studies in education and public policy, environmental pollution, agricul­ ture, and robotics. INVITED PAPERS The three invited cases studies at the workshop discuss problems in ed­ ucational policy, clinical trials design, and environmental epidemiology, respectively. 1. In School Choice in NY City: A Bayesian Analysis ofan Imperfect Randomized Experiment J. Barnard, C. Frangakis, J. Hill, and D. Rubin report on the analysis of the data from a randomized study conducted to evaluate the New YorkSchool Choice Scholarship Pro­ gram.

Advanced Statistical Methods for Astrophysical Probes of Cosmology (Repost)

Posted By: AvaxGenius
Advanced Statistical Methods for Astrophysical Probes of Cosmology (Repost)

Advanced Statistical Methods for Astrophysical Probes of Cosmology by Marisa Cristina March
English | PDF | 2013 | 192 Pages | ISBN : 3642350593 | 4.3 MB

This thesis explores advanced Bayesian statistical methods for extracting key information for cosmological model selection, parameter inference and forecasting from astrophysical observations. Bayesian model selection provides a measure of how good models in a set are relative to each other - but what if the best model is missing and not included in the set? Bayesian Doubt is an approach which addresses this problem and seeks to deliver an absolute rather than a relative measure of how good a model is. Supernovae type Ia were the first astrophysical observations to indicate the late time acceleration of the Universe - this work presents a detailed Bayesian Hierarchical Model to infer the cosmological parameters (in particular dark energy) from observations of these supernovae type Ia.

Machine Learning Algorithm for Fatigue Fields in Additive Manufacturing

Posted By: AvaxGenius
Machine Learning Algorithm for Fatigue Fields in Additive Manufacturing

Machine Learning Algorithm for Fatigue Fields in Additive Manufacturing by Mustafa Mamduh Mustafa Awd
English | PDF,EPUB | 2022 | 289 Pages | ISBN : 3658402369 | 78.1 MB

Fatigue failure of structures used in transportation, industry, medical equipment, and electronic components needs to build a link between cutting-edge experimental characterization and probabilistically grounded numerical and artificially intelligent tools. The physics involved in this process chain is computationally prohibitive to comprehend using traditional computation methods. Using machine learning and Bayesian statistics, a defect-correlated estimate of fatigue strength was developed. Fatigue, which is a random variable, is studied in a Bayesian-based machine learning algorithm. The stress-life model was used based on the compatibility condition of life and load distributions. The defect-correlated assessment of fatigue strength was established using the proposed machine learning and Bayesian statistics algorithms. It enabled the mapping of structural and process-induced fatigue characteristics into a geometry-independent load density chart across a wide range of fatigue regimes.

Bayesian Statistics and Marketing

Posted By: AvaxGenius
Bayesian Statistics and Marketing

Bayesian Statistics and Marketing by Peter E. Rossi, Greg M. Allenby, Robert McCulloch
English | PDF | 2005 | 364 Pages | ISBN : 0470863676 | 7.6 MB

The past decade has seen a dramatic increase in the use of Bayesian methods in marketing due, in part, to computational and modelling breakthroughs, making its implementation ideal for many marketing problems. Bayesian analyses can now be conducted over a wide range of marketing problems, from new product introduction to pricing, and with a wide variety of different data sources.

Modern Bayesian Statistics in Clinical Research (Repost)

Posted By: AvaxGenius
Modern Bayesian Statistics in Clinical Research (Repost)

Modern Bayesian Statistics in Clinical Research by Ton J. Cleophas
English | PDF | 2018 | 193 Pages | ISBN : 3319927469 | 5.9 MB

The current textbook has been written as a help to medical / health professionals and students for the study of modern Bayesian statistics, where posterior and prior odds have been replaced with posterior and prior likelihood distributions. Why may likelihood distributions better than normal distributions estimate uncertainties of statistical test results? Nobody knows for sure, and the use of likelihood distributions instead of normal distributions for the purpose has only just begun, but already everybody is trying and using them.
SPSS statistical software version 25 (2017) has started to provide a combined module entitled Bayesian Statistics including almost all of the modern Bayesian tests (Bayesian t-tests, analysis of variance (anova), linear regression, crosstabs etc.).

A Student's Guide to Bayesian Statistics

Posted By: IrGens
A Student's Guide to Bayesian Statistics

A Student's Guide to Bayesian Statistics by Ben Lambert
English | August 22, 2018 | ISBN: 1473916356, 1473916364 | PDF | 520 pages | 22.53 MB

Think Bayes: Bayesian Statistics in Python, 2nd Edition

Posted By: yoyoloit
Think Bayes: Bayesian Statistics in Python, 2nd Edition

Think Bayes
by Downey, Allen B.;

English | 2021 | ISBN: 149208946X | 338 pages | True PDF | 18.47 MB

Bayesian Statistics the Fun Way

Posted By: hill0
Bayesian Statistics the Fun Way

Bayesian Statistics the Fun Way:
Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks
by Will Kurt

English | 2019 | ISBN: 1593279566 | 290 Pages | PDF True | 7 MB

Bayesian Statistics for Experimental Scientists: A General Introduction Using Distribution-Free Methods

Posted By: sasha82
Bayesian Statistics for Experimental Scientists: A General Introduction Using Distribution-Free Methods

Bayesian Statistics for Experimental Scientists: A General Introduction Using Distribution-Free Methods by Richard A. Chechile
September 8, 2020 | ISBN: 0262044587 | English | 512 pages | PDF | 21 MB

Statistical Modelling and Regression Structures: Festschrift in Honour of Ludwig Fahrmeir (Repost)

Posted By: AvaxGenius
Statistical Modelling and Regression Structures: Festschrift in Honour of Ludwig Fahrmeir (Repost)

Statistical Modelling and Regression Structures: Festschrift in Honour of Ludwig Fahrmeir by Thomas Kneib
English | PDF | 2010 | 486 Pages | ISBN : 3790824127 | 18.31 MB

The contributions collected in this book have been written by well-known statisticians to acknowledge Ludwig Fahrmeir's far-reaching impact on Statistics as a science, while celebrating his 65th birthday. The contributions cover broad areas of contemporary statistical model building, including semiparametric and geoadditive regression, Bayesian inference in complex regression models, time series modelling, statistical regularization, graphical models and stochastic volatility models.

Data Assimilation: A Mathematical Introduction

Posted By: AvaxGenius
Data Assimilation: A Mathematical Introduction

Data Assimilation: A Mathematical Introduction by Kody Law
English | PDF(Repost),EPUB | 2015 | 256 Pages | ISBN : 331920324X | 10 MB

This book provides a systematic treatment of the mathematical underpinnings of work in data assimilation, covering both theoretical and computational approaches. Specifically the authors develop a unified mathematical framework in which a Bayesian formulation of the problem provides the bedrock for the derivation, development and analysis of algorithms; the many examples used in the text, together with the algorithms which are introduced and discussed, are all illustrated by the MATLAB software detailed in the book and made freely available online.