25 papers · ranked by Valyu relevance
Juan Sosa, Carlos Martínez, Cruz, Danna
This paper offers a comprehensive introduction to Bayesian inference, combining historical context, theoretical foundations, and core analytical examples. Beginning with Bayes' theorem and the philosophical distinctions between Bayesian and frequentist approaches, we develop the inferential framework for estimation…
Sebastian Sosa, Mary Brooke McElreath, Cody Ross
Bayesian modeling is a cornerstone of modern ecological and evolutionary research, offering the flexibility to account for hierarchical structures, imperfect detection, and spatial dependencies. However, as ecological datasets grow in scale and complexity—from high-resolution telemetry to phylogenomics—researchers…
František Bartoš, Eric-Jan Wagenmakers, Maarten Marsman, Don van den Bergh
Bayes factor sensitivity analysis examines how the evidence for one hypothesis over another depends on the prior distribution. In complex models, the standard approach refits the model at each hyper-parameter value, and the total computational cost scales linearly in the grid size. We propose a method that recovers the…
Sarah Schreiber, Danielle Hewitt, Ben Seymour, Wako Yoshida
Bayesian statistics offers a flexible framework that supports iterative updating of hypotheses and the incorporation of prior information, amongst other advantages. Although well established for retrospective analysis, the application of Bayesian methods to prospective analysis is less well developed, especially when…
Camila Natalia Barragan Ibañez, Ulrich Lösener, Nnamdi Moeteke, Mirjam Moerbeek + 1 more
To study the effect of a behavioral intervention, it should be compared to a control or an existing treatment in an intervention study. There exist many guidelines in the literature about the design and analysis of intervention studies, including recommendations for a priori sample size determination. The vast majority…
Daniel K. Sewell, Alan T. Arakkal
Bayesian statistics is an integral part of contemporary applied science. bayesics provides a single framework, unified in syntax and output, for performing the most commonly used statistical procedures, ranging from one- and two-sample inference to general mediation analysis. bayesics leans hard away from the…
Carl J. Stone, Megan G. Behringer
Temporally structured environments are ubiquitous in nature, but time-dependent fitness effects are difficult to measure and thus understudied. To resolve temporal fitness structure at genome scale, we developed a Bayesian multilevel framework for longitudinal randomly barcoded transposon sequencing (RB-TnSeq) that…
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The political, social and economic consequences of climate change drastically influence the requirements of modern energy systems and its components. This includes not only energy production but also concepts and innovations for its storage, especially in magnitudes of gigawatt hours. Carnot batteries, which convert…
Daniel Lüdecke, Anna C. Makowski, Jens Klein, Mattan S. Ben-Shachar + 1 more
Background Bayesian regression models provide a robust framework for complex data analysis, which is particularly advantageous in scenarios with small sample sizes, common in psychology or medical research. However, specifying appropriate prior distributions that incorporate existing knowledge to regularize model…
Priscilla Balestrucci, Maura Mezzetti, Barbara La Scaleia, Alessandro Moscatelli
Inferential models in psychophysics are essential for quantifying the relation between physical properties of the stimulus and their perceptual representations. The psychometric function is typically used to model the responses of individual participants in forced-choice experiments. The accuracy and the noise of the…
Ohigashi, Tomohiro, Sugasawa, Shonosuke
Bayesian methods have received increasing attention in medical research, where sensitivity analysis of prior distributions is essential. Such analyses typically require the evaluation of the posterior distribution of a parameter under multiple alternative prior settings. When the posterior distribution of the parameter…
Arina Odnoblyudova, Charita Dellaporta, François-Xavier Briol
Bayesian inference can often be sensitive to the choice of hyperparameters of the prior or likelihood, yet defining and quantifying this sensitivity in a principled and computationally feasible way remains challenging in practice. Unfortunately, existing sensitivity methods are rarely applicable in modern Bayesian…
Šimon Kucharský, Eric-Jan Wagenmakers, Don van den Bergh, Alexander Ly
This article outlines a novel Bayesian approach to the testing and estimation of Pearson partial correlations. By generalizing a Bayesian inference procedure for Pearson’s correlation coefficient, we obtain analytic expressions for the Bayes factor and for the (marginal) posterior distribution of a partial correlation…
Wenqisi Pan, Zeyu Lu, Wei Jiang, Johan Lim + 2 more
In meta-analyses of continuous outcomes, the sample mean and standard deviation (SD) are essential for synthesizing effect sizes across studies. However, clinical studies frequently report alternative summary statistics, such as the median, quartiles, and range. To enable inclusion of such studies, various methods have…
Sarfaraz K. Niazi
The U.S. Food and Drug Administration released in January 2026 a draft guidance on the use of Bayesian methodology in clinical trials of drugs and biological products, representing a significant evolution in its regulatory approach to evaluating evidence supporting marketing authorization. The guidance reflects a…
Saurabh Panchasara, Hanna Jankowski, Kevin McGregor
Advancements in next-generation sequencing have transformed our understanding of host-microbe interactions, revealing links between microbial composition and chronic conditions such as obesity, diabetes, IBD, and others. However, the analysis of microbiome data is complex due to its unique statistical characteristics.…
Stefano Dalla Bona, Andrea Spoto, Marta Caserotti, Lorella Lotto + 4 more
Empirical investigation requires dealing with fundamental uncertainty. In experimental psychology, research questions are often addressed using Null Hypothesis Significance Testing (NHST), an approach rooted in the frequentist statistical tradition. In scenarios that do not consent to reject the null hypothesis using…
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Occupational chemical hazards pose profound risks to chemists in laboratory and industrial settings, encompassing acute and chronic exposures that imperil sensory organs (e.g., ocular, auditory, olfactory, dermal) and vital physiological systems. This manuscript delineates a multifaceted, innovative protocol suite…
Lukas Ramlow, Jasmin Scholtes, Daniel Danis, Peter N. Robinson
We present Ontologizer 3, an easy-to-use cross-platform desktop application for Gene Ontology (GO) overrepresentation analysis. Ontologizer 3 offers two complementary methods. The first is a frequentist approach that evaluates GO terms individually using a one-sided Fisher’s exact test, yielding term-level significance…
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Incorporating prior domain knowledge into Bayesian optimization (BO) remains difficult for statistical methods, which also typically suffer from limited interpretability. Large language models (LLMs) offer complementary strengths in reasoning and knowledge integration, but it remains unclear when and how they improve…
Michael Evans, Siqi Zheng
Probability theory provides a clear definition of what is meant by evidence in favor, against or none either way, of an event occurring for an unobserved response, via the principle of evidence. This is immediately applicable when carrying out a proper Bayesian analysis. Even without a prior, this imposes restrictions…
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Thorough treatment of conformation in computational chemistry is required to capture the subtle energy differences that lead to experimental observations. Accurate quantum chemistry calculations are very expensive and evaluation of the entire ensemble found during a conformational search is often unachievable. This is…
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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…
Valerian V. Popkov, Geert Verdoolaege
Bayesian inference is predominantly formulated in a continuous framework, in which posterior beliefs are represented by smooth probability densities. However, an alternative discrete representation-already implicit in Bayes’s original construction-remains conceptually distinct and structurally informative. This paper…
Authors not listed
Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…