scholarly journals Bayesian data analysis for newcomers

2017 ◽  
Author(s):  
John K. Kruschke ◽  
Torrin Liddell
Keyword(s):  
Data Analysis ◽  
Bayesian Analysis ◽  
Bayesian Methods ◽  
Mathematical Notation ◽  
Value Assessment ◽  
Clear Sense ◽  
Bayesian Data Analysis ◽  
Null Value ◽  
Methodological Problems ◽  
Everyday Reasoning

This article explains the foundational concepts of Bayesian data analysis using virtually no mathematical notation. Bayesian ideas already match your intuitions from everyday reasoning and from traditional data analysis. Simple examples of Bayesian data analysis are presented, that illustrate how the information delivered by a Bayesian analysis can be directly interpreted.Bayesian approaches to null-value assessment are discussed. The article clarifies misconceptions about Bayesian methods that newcomers might have acquired elsewhere. We discuss prior distributions and explain how they are not a liability but an important asset. We discussthe relation of Bayesian data analysis to Bayesian models of mind, and we briefly discuss what methodological problems Bayesian data analysis is not meant to solve. After you have read this article, you should have a clear sense of how Bayesian data analysis works and the sort of information it delivers, and why that information is so intuitive and useful for drawing conclusions from data.

scholarly journals Introdução à estatística bayesiana aplicada à linguística

2021 ◽  
Author(s):  
Guilherme D. Garcia ◽  
Ronaldo Mangueira Lima Jr
Keyword(s):  
Data Analysis ◽  
Simple Model ◽  
Bayesian Analysis ◽  
Regression Model ◽  
Bayesian Approach ◽  
Linguistic Data ◽  
Bayesian Data Analysis ◽  
Frequentist Statistics

Neste artigo, apresentamos os conceitos básicos de uma análise estatística bayesiana e demonstramos como rodar um modelo de regressão utilizando a linguagem R. Ao longo do artigo, comparamos estatística bayesiana e estatística frequentista, destacamos as diferentes vantagens apresentadas por uma abordagem bayesiana, e demonstramos como rodar um modelo simples e visualizar efeitos de interesse. Por fim, sugerimos leituras adicionais aos interessados neste tipo de análise.In this paper, we introduce the basics of Bayesian data analysis and demonstrate how to run a regression model in R using linguistic data. Throughout the paper, we compare Bayesian and Frequentist statistics, highlighting the different advantages of a Bayesian approach. We also show how to run a simple model and how to visualize effects of interest. Finally, we suggest additional readings to those interested in Bayesian analysis more generally.

scholarly journals Bayesian analysis in social sciences

2021 ◽  
Author(s):  
Minh-Hoang Nguyen
Keyword(s):  
Social Sciences ◽  
Data Analysis ◽  
Bayesian Inference ◽  
Bayesian Analysis ◽  
Bayesian Data Analysis ◽  
Replication Crisis ◽  
Current Article

Given the reproducibility crisis (or replication crisis), more psychologists and social-cultural scientists are getting involved with Bayesian inference. Therefore, the current article provides a brief overview of programs (or software) and steps to conduct Bayesian data analysis in social sciences.

Principles of Bayesian Methods in Data Analysis

2010 ◽  
Vol 437 ◽  
pp. 3-7
Author(s):  
Michael Paul Krystek
Keyword(s):  
Data Analysis ◽  
Bayesian Methods ◽  
Prior Information ◽  
Probability Density Functions ◽  
Density Functions ◽  
Uncertainty In Measurement ◽  
Bayesian Data Analysis ◽  
Expected Values ◽  
Expression Of Uncertainty ◽  
Natural Way

Bayesian statistics provides a powerful tool for the analysis of data. The methods are flexible enough to permit a realistic modelling of complex measurements. Prior information about the experiment, as well as knowledge from other sources can be used in a natural way. All relevant quantities concerning the measurement, as e. g. the expected values and their associated uncertainties are obtained from probability density functions. Bayesian data analysis strictly follows the rules of probability theory, thus ensuring that the procedure is free of inconsistencies and is in accordance with the Guide to the Expression of Uncertainty in Measurement (GUM).

Accuracy and sensitivity of different Bayesian methods for genomic prediction using simulation and real data

2020 ◽  
Vol 19 (3) ◽  
Author(s):  
Saheb Foroutaifar
Keyword(s):  
Data Analysis ◽  
Bayesian Methods ◽  
Milk Fat ◽  
Prediction Accuracy ◽  
Real Data ◽  
Fat Percentage ◽  
Real Data Analysis ◽  
Wide Range ◽  
High Heritability ◽  
Qtl Effects

AbstractThe main objectives of this study were to compare the prediction accuracy of different Bayesian methods for traits with a wide range of genetic architecture using simulation and real data and to assess the sensitivity of these methods to the violation of their assumptions. For the simulation study, different scenarios were implemented based on two traits with low or high heritability and different numbers of QTL and the distribution of their effects. For real data analysis, a German Holstein dataset for milk fat percentage, milk yield, and somatic cell score was used. The simulation results showed that, with the exception of the Bayes R, the other methods were sensitive to changes in the number of QTLs and distribution of QTL effects. Having a distribution of QTL effects, similar to what different Bayesian methods assume for estimating marker effects, did not improve their prediction accuracy. The Bayes B method gave higher or equal accuracy rather than the rest. The real data analysis showed that similar to scenarios with a large number of QTLs in the simulation, there was no difference between the accuracies of the different methods for any of the traits.

A Bayesian Data Analysis System for the Evaluation of Social Programs

1977 ◽  
Vol 72 (360) ◽  
pp. 711 ◽  
Author(s):  
Ming-Mei Wang ◽  
Melvin R. Novick ◽  
Gerald L. Isaacs ◽  
Dan Ozenne
Keyword(s):  
Data Analysis ◽  
Social Programs ◽  
Bayesian Data Analysis ◽  
Analysis System ◽  
Data Analysis System

scholarly journals Bayesian data analysis in the phonetic sciences: A tutorial introduction

2018 ◽  
Vol 71 ◽  
pp. 147-161 ◽  
Author(s):  
Shravan Vasishth ◽  
Bruno Nicenboim ◽  
Mary E. Beckman ◽  
Fangfang Li ◽  
Eun Jong Kong
Keyword(s):  
Data Analysis ◽  
Bayesian Data Analysis

Mathematics and Statistics in Anesthesiology

2018 ◽  
Author(s):  
Daniel Mortlock
Keyword(s):  
Probability Theory ◽  
Data Analysis ◽  
Survey Design ◽  
Probability Distributions ◽  
Statistical Tests ◽  
Real World Data ◽  
Mathematical Functions ◽  
P Values ◽  
Bayesian Data Analysis ◽  
Probability And Statistics

Mathematics is the language of quantitative science, and probability and statistics are the extension of classical logic to real world data analysis and experimental design. The basics of mathematical functions and probability theory are summarized here, providing the tools for statistical modeling and assessment of experimental results. There is a focus on the Bayesian approach to such problems (ie, Bayesian data analysis); therefore, the basic laws of probability are stated, along with several standard probability distributions (eg, binomial, Poisson, Gaussian). A number of standard classical tests (eg, p values, the t-test) are also defined and, to the degree possible, linked to the underlying principles of probability theory. This review contains 5 figures, 1 table, and 15 references. Keywords: Bayesian data analysis, mathematical models, power analysis, probability, p values, statistical tests, statistics, survey design

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