15 papers · ranked by Valyu relevance
Eric-Jan Wagenmakers, Maarten Marsman, Tahira Jamil, Alexander Ly + 9 more
Bayesian parameter estimation and Bayesian hypothesis testing present attractive alternatives to classical inference using confidence intervals and p values. In part I of this series we outline ten prominent advantages of the Bayesian approach. Many of these advantages translate to concrete opportunities for pragmatic…
Erling W. Rognli, Rune Zahl‐Olsen, Sondre Sverd Rekdal, Asle Hoffart + 1 more
'Thomas Bjerregaard Bertelsen'] Bayesian statistical approaches offer nuanced, detailed, and intuitive analyses, even with small sample sizes. Although these qualities are highly relevant for researchers in child and adolescent mental health, Bayesian methods are still quite rarely employed. This editorial perspective…
Branimir K. Hackenberger
According to different scientific literature databases (eg, ScienceDirect, Web of Science), each year more and more scientific articles use Bayesian methods for data processing ([F1]). Does this mean that Bayesian statistics is better than frequentist statistics? What can we achieve with Bayesian methods but not with…
Christopher Yau, Kieran Campbell
Bayesian statistical learning provides a coherent probabilistic framework for modelling uncertainty in systems. This review describes the theoretical foundations underlying Bayesian statistics and outlines the computational frameworks for implementing Bayesian inference in practice. We then describe the use of Bayesian…
Jakub Bijak, John Bryant
Bayesian statistics offers an alternative to classical (frequentist) statistics. It is distinguished by its use of probability distributions to describe uncertain quantities, which leads to elegant solutions to many difficult statistical problems. Although Bayesian demography, like Bayesian statistics more generally…
Xuan Guo, Bing Liu, Le Chen, Guantao Chen + 2 more
This paper aims to review state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI) data. Particularly, we focus on one specific long-standing challenge in the computational modeling of fMRI datasets: how to effectively explore typical functional interactions from fMRI…
Callum Taylor, Kathryn Puxty, Tara Quasim, Martin Shaw
Bayesian analysis is being used with increasing frequency in critical care research and brings advantages and disadvantages compared to traditional Frequentist techniques. This study overviews this methodology and explains the terminology encountered when appraising this literature. Setting different priors can impact…
Udi Alter, Miranda A. Too, Robert A. Cribbie
Bayesian statistics has gained substantial popularity in the social sciences, particularly in psychology. Despite its growing prominence in the psychological literature, many researchers remain unacquainted with Bayesian methods and their advantages. This tutorial addresses the needs of curious applied psychology…
Jure Demšar, Grega Repovš, Erik Štrumbelj
Research in psychology generates complex data and often requires unique statistical analyses. These tasks are often very specific, so appropriate statistical models and methods cannot be found in accessible Bayesian tools. As a result, the use of Bayesian methods is limited to researchers and students that have the…
Samidha Shetty, Gordon Brittan Jr., Prasanta S. Bandyopadhyay, Dawn E. Holmes
'Dawn E. Holmes'] Empirical Bayes-based Methods (EBM) is an increasingly popular form of Objective Bayesianism (OB). It is identified in particular with the statistician Bradley Efron. The main aims of this paper are, first, to describe and illustrate its main features and, second, to locate its role by comparing it…
Brandon S. Coventry, Edward L. Bartlett
Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in the replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian…
Michele Introna, Johannes P. van den Berg, Douglas J. Eleveld, Michel M. R. F. Struys
'Michel M. R. F. Struys'] This narrative review intends to provide the anesthesiologist with the basic knowledge of the Bayesian concepts and should be considered as a tutorial for anesthesiologists in the concept of Bayesian statistics. The Bayesian approach represents the mathematical formulation of the idea that we…
Rens van de Schoot, Naomi Schalken, Miranda Olff
2.1.### Flexible hypothesis testing The first reason why Bayesian statistics is used is that it provides a flexible alternative to null hypothesis significance testing. van Veen, Engelhard, and van den Hout ([CIT0036]) tested several predictions from working memory theory that could explain the efficacy of eye movement…
Jose D. Perezgonzalez
Bayesian Statistics for Beginners. A Step-by-Step Approach (Donovan and Mickey, [2]) is, perhaps, the “truest-to-title” book I have read on Bayesian inference and statistics, insofar (a) it is written for novices to probability, inference, the scientific method, and Bayesian methodology, (b) it introduces those four…
Michael Evans, Yang Guo, Jacinto Martín, María Isabel Parra Arévalo
A common concern with Bayesian methodology in scientific contexts is that inferences can be heavily influenced by subjective biases. As presented here, there are two types of bias for some quantity of interest: bias against and bias in favor. Based upon the principle of evidence, it is shown how to measure and control…