Search · four archives

Results for Bayesian inference

or ask it as a questionAsk the stacks →

ArchivesarXiv PubMed bioRxiv chemRxiv

WhenAny time Past month Past year Past 5 years

OrderMost relevant Most cited Newest

21 papers · ranked by Valyu relevance

01
PPubMed1 Mar 2016
Bayesian Inference for Functional Dynamics Exploring in fMRI Data
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…
93relevance
02
PPubMed19 Dec 2024
Navigating the Bayes maze: The psychologist's guide to Bayesian statistics, a hands‐on tutorial with R code NAVIGATING THE BAYES MAZE Alter, Too, Cribbie
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…
92relevance
03
BbioRxiv2 Dec 2019
A new method of Bayesian causal inference in non-stationary environments
Shuji Shinohara, Nobuhito Manome, Kouta Suzuki, Ung-il Chung + 5 more
Bayesian inference is a process of narrowing down hypotheses (causes) to one that best explains observational data (effects). To accurately estimate a cause, a considerable amount of data is required to be observed for as long as possible. However, the object of inference is not always constant. In this case, a method…
91relevance
04
BbioRxiv21 Nov 2023
Practical Bayesian Inference in Neuroscience: Or How I Learned To Stop Worrying and Embrace the Distribution
Brandon S Coventry, Edward L Bartlett
Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in 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…
91relevance
05
PPubMed11 Apr 2018
Bayesian evaluation of behavior change interventions: a brief introduction and a practical example HEALTH PSYCHOLOGY AND BEHAVIORAL MEDICINE M. T. HEINO ET AL.
Matti T. J. Heino, Matti Vuorre, Nelli Hankonen
Introduction Evaluating effects of behavior change interventions is a central interest in health psychology and behavioral medicine. Researchers in these fields routinely use frequentist statistical methods to evaluate the extent to which these interventions impact behavior and the hypothesized mediating processes in…
91relevance
06
PPubMed22 May 2020
A new method of Bayesian causal inference in non-stationary environments A new method of Bayesian causal inference in non-stationary environments
Shuji Shinohara, Nobuhito Manome, Kouta Suzuki, Ung-il Chung + 6 more
'Tatsuji Takahashi' 'Hiroshi Okamoto' 'Yukio Pegio Gunji' 'Yoshihiro Nakajima' 'Shunji Mitsuyoshi' 'Enrico Scalas'] Bayesian inference is the process of narrowing down the hypotheses (causes) to the one that best explains the observational data (effects). To accurately estimate a cause, a considerable amount of data is…
90relevance
07
BbioRxiv10 Jul 2019
Extended Bayesian inference incorporating symmetry bias
Shuji Shinohara, Nobuhito Manome, Kouta Suzuki, Ung-il Chung + 4 more
In this study, we start by proposing a causal induction model that incorporates symmetry bias. This model has two parameters that control the strength of symmetry bias and includes conditional probability and conventional models of causal induction as special cases. It can reproduce causal induction of human judgment…
90relevance
08
AarXiv2017
Bayesian Methods in Cosmology
Roberto Trotta
These notes aim at presenting an overview of Bayesian statistics, the underlying concepts and application methodology that will be useful to astronomers seeking to analyse and interpret a wide variety of data about the Universe. The level starts from elementary notions, without assuming any previous knowledge of…
90relevance
09
CchemRxiv1 Oct 2018
Separating the Effects of Experimental Noise from Inherent System Variability in Voltammetry: The [Fe(CN)_6]^3−/4− Process
Martin Robinson, Alan Bond, Alexandr Simonov, Jie Zhang + 1 more
Recently, we have introduced the use of techniques drawn from Bayesian statistics to recover kinetic and thermodynamic parameters from voltammetric data, and were able to show that the technique of large amplitude ac voltammetry yielded significantly more accurate parameter values than the equivalent dc approach. In…
88relevance
10
AarXiv2012
Ensemble filter techniques for intermittent data assimilation - a survey
Colin J. Cotter, Sebastian Reich
This survey paper is written with the intention of giving a mathematical introduction to filtering techniques for intermittent data assimilation, and to survey some recent advances in the field. The paper is divided into three parts. The first part introduces Bayesian statistics and its application to statistical…
87relevance
11
AarXiv2023
Inferential Moments of Uncertain Multivariable Systems
Kevin Vanslette
This article expands the framework of Bayesian inference and provides direct probabilistic methods for approaching inference tasks that are typically handled with information theory. We treat Bayesian probability updating as a random process and uncover intrinsic quantitative features of joint probability distributions…
86relevance
12
PPubMed14 Jun 2022
Making Sense of Uncertainty in the Science Classroom A Bayesian Approach
Joshua M. Rosenberg, Marcus Kubsch, Eric-Jan Wagenmakers, Mine Dogucu
Uncertainty is ubiquitous in science, but scientific knowledge is often represented to the public and in educational contexts as certain and immutable. This contrast can foster distrust when scientific knowledge develops in a way that people perceive as a reversals, as we have observed during the ongoing COVID-19…
86relevance
13
AarXiv2021
Bayesian Learning: A Selective Overview
Yu Lin Hsu, Chu Chuan Jeng, Pavithra Sripathanallur Murali, Mohammadreza Torkjazi + 3 more
'Mohammadreza Torkjazi' 'J. S. West' 'Michaela Zuber' 'Vadim Sokolov'] This paper presents an overview of some of the concepts of Bayesian Learning. The number of scientific and industrial applications of Bayesian learning has been growing in size rapidly over the last few decades (Damien et al. 2013). This process has…
84relevance
14
AarXiv2023
The fiducial-Bayes fusion: A general theory of statistical inference
Russell J. Bowater
An overview is presented of a general theory of statistical inference that is referred to as the fiducial-Bayes fusion. This theory combines organic fiducial inference and Bayesian inference. The aim is that the reader is given a clear summary of the conceptual framework of the fiducial-Bayes fusion as well as pointers…
84relevance
15
AarXiv2013
Second Order Probabilities for Uncertain and Conflicting Evidence
Gerhard Paaß
In this paper the elicitation of probabilities from simultaneously meets all constraints. The ratiohuman experts is considered as a measurement process, which may be disturbed by random ·measurement noise'. Using Bayesian concepts a second order probability distribution is derived reflecting the uncertainty of the…
81relevance
16
PPubMed18 Nov 2020
Why optional stopping can be a problem for Bayesians
Rianne de Heide, Peter D. Grünwald
Recently, optional stopping has been a subject of debate in the Bayesian psychology community. Rouder (Psychonomic Bulletin & Review*21(2), 301-308, [33]) argues that optional stopping is no problem for Bayesians, and even recommends the use of optional stopping in practice, as do (Wagenmakers, Wetzels, Borsboom, van…
81relevance
17
CchemRxiv18 Oct 2022
Copolymerization Reactivity Ratio Inference: Determining Confidence Contours in Parameter Space via a Bayesian Hierarchical Approach
Robert Reischke
Confidence contours in parameter space are a helpful tool to compare and classify determined estimators. For more intricate parameter estimations of non-linear nature or complex error structures, the procedure of determining confidence contours is a statistically complex task. For polymer chemists, such particular…
81relevance
18
BbioRxiv11 Dec 2020
Choosing priors in Bayesian ecological models by simulating from the prior predictive distribution
Jeff S. Wesner, Justin P.F. Pomeranz
Bayesian data analysis is increasingly used in ecology, but prior specification remains focused on choosing non-informative priors (e.g., flat or vague priors). One barrier to choosing more informative priors is that priors must be specified on model parameters (e.g., intercepts, slopes, sigmas), but prior knowledge…
78relevance
19
CchemRxiv15 Jan 2026
Uncertainty Quantification for In Silico Chemistry
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
70relevance
20
CchemRxiv23 Jun 2021
Bayesian optimization of nanoporous materials
Aryan Deshwal, Cory Simon, Janardhan Rao Doppa
Given a gas storage or separation task, we wish to search a library of nanoporous materials (NPMs) for the one with the optimal adsorption property. The high cost of measuring the adsorption property of an NPM, whether in the lab or a simulation, precludes exhaustive search. We explain, demonstrate, and advocate…
69relevance
21
CchemRxiv6 Dec 2023
Race to the bottom: Bayesian optimisation for chemical problems
Yifan Wu, Aron Walsh, Alex Ganose
What is the minimum number of experiments, or calculations, required to find an optimal solution? Relevant chemical problems range from identifying a compound with target functionality within a given phase space to controlling materials synthesis and device fabrication conditions. A common feature in this application…
68relevance

Results for Bayesian inference

Bayesian Inference for Functional Dynamics Exploring in fMRI Data

Navigating the Bayes maze: The psychologist's guide to Bayesian statistics, a hands‐on tutorial with R code NAVIGATING THE BAYES MAZE Alter, Too, Cribbie

A new method of Bayesian causal inference in non-stationary environments

Practical Bayesian Inference in Neuroscience: Or How I Learned To Stop Worrying and Embrace the Distribution

Bayesian evaluation of behavior change interventions: a brief introduction and a practical example HEALTH PSYCHOLOGY AND BEHAVIORAL MEDICINE M. T. HEINO ET AL.

A new method of Bayesian causal inference in non-stationary environments A new method of Bayesian causal inference in non-stationary environments

Extended Bayesian inference incorporating symmetry bias

Bayesian Methods in Cosmology

Separating the Effects of Experimental Noise from Inherent System Variability in Voltammetry: The [Fe(CN)_6]^3−/4− Process

Ensemble filter techniques for intermittent data assimilation - a survey

Inferential Moments of Uncertain Multivariable Systems

Making Sense of Uncertainty in the Science Classroom A Bayesian Approach

Bayesian Learning: A Selective Overview

The fiducial-Bayes fusion: A general theory of statistical inference

Second Order Probabilities for Uncertain and Conflicting Evidence

Why optional stopping can be a problem for Bayesians

Copolymerization Reactivity Ratio Inference: Determining Confidence Contours in Parameter Space via a Bayesian Hierarchical Approach

Choosing priors in Bayesian ecological models by simulating from the prior predictive distribution

Uncertainty Quantification for In Silico Chemistry

Bayesian optimization of nanoporous materials

Race to the bottom: Bayesian optimisation for chemical problems

Bayesian Inference for Functional Dynamics Exploring in fMRI Data

Navigating the Bayes maze: The psychologist's guide to Bayesian statistics, a hands‐on tutorial with R code NAVIGATING THE BAYES MAZE Alter, Too, Cribbie

A new method of Bayesian causal inference in non-stationary environments

Practical Bayesian Inference in Neuroscience: Or How I Learned To Stop Worrying and Embrace the Distribution

Bayesian evaluation of behavior change interventions: a brief introduction and a practical example HEALTH PSYCHOLOGY AND BEHAVIORAL MEDICINE M. T. HEINO ET AL.

A new method of Bayesian causal inference in non-stationary environments A new method of Bayesian causal inference in non-stationary environments

Extended Bayesian inference incorporating symmetry bias

Bayesian Methods in Cosmology

Separating the Effects of Experimental Noise from Inherent System Variability in Voltammetry: The [Fe(CN)_6]^3−/4− Process

Ensemble filter techniques for intermittent data assimilation - a survey

Inferential Moments of Uncertain Multivariable Systems

Making Sense of Uncertainty in the Science Classroom A Bayesian Approach

Bayesian Learning: A Selective Overview

The fiducial-Bayes fusion: A general theory of statistical inference

Second Order Probabilities for Uncertain and Conflicting Evidence

Why optional stopping can be a problem for Bayesians

Copolymerization Reactivity Ratio Inference: Determining Confidence Contours in Parameter Space via a Bayesian Hierarchical Approach

Choosing priors in Bayesian ecological models by simulating from the prior predictive distribution

Uncertainty Quantification for In Silico Chemistry

Bayesian optimization of nanoporous materials

Race to the bottom: Bayesian optimisation for chemical problems