27 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…
Julia Reuter, Fabricio Olivetti de Franca
Symbolic regression (SR) is a class of methods that systematically explore the space of mathematical functions to discover models that accurately capture the underlying relationships in a dataset. Despite recent advances in the field, a lack of support for uncertainty quantification (UQ) limits its adoption in…
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…
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…
Suzie Cro, Zhangyi He, Daphne Babalis, Laurent Billot
Background and Aims A Study Within A Trial (SWAT) is a research study embedded within a larger trial which aims to investigate different strategies for a particular trial process, such as trial recruitment. It is imperative such studies, which are often underpowered, employ efficient and informative analytical methods.…
Nadav Ben Nun, Saharon Rosset, David Gresham, Yoav Ram
High-throughput experimental platforms now routinely generate data from dozens or hundreds of independent observations. Simulation-based inference (SBI) offers a powerful framework for estimating model parameters from such complex datasets, but standard methods struggle to scale to the noisy multiple-replicates regime…
Anna-Lena Tebbe, Christian Panitz, Andreas Keil
Electroencephalography (EEG) is a widely used method for investigating human brain dynamics. However, EEG analyses are frequently conducted with limited a priori knowledge regarding locations or latencies of meaningful statistical effects. This makes it difficult for researchers to form regions of interest (ROIs)…
Francesco G. Rinaldi, Eugenio Piasini
To make sense of a noisy world, living beings constantly face decisions between competing interpretations for ambiguous sensory data. This process parallels statistical model selection, where most frameworks, like the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), are based on a…
Nils Lid Hjort, Chris Holmes, Peter Mueller, Stephen G. Walker
This extended preface [to the Book `Bayesian Nonparametrics', Cambridge University Press, 2010, by NL Hjort, CC Holmes, P Mueller, SG Walker] is meant to explain why you are right to be curious about Bayesian nonparametrics -- why you may actually need it and how you can manage to understand it and use it. The preface…
Evan A. Martin, Venkata Patchigolla, Audrey Qiuyan Fu, Jifan Shi
Graphical models are widely used to represent dependence structures in biological systems, where directed edges may encode causal relationships under appropriate assumptions. We present baycn (BAYesian Causal Network), a novel approximate Bayesian method for inferring probabilities of edge directions and edge absence…
Zixiao Hu, Jason D. McEwen
Bayesian model comparison implements Occam's razor through its sensitivity to the prior. However, priordependence makes it important to assess the influence of plausible alternative priors. Such prior sensitivity analyses for the Bayesian evidence are expensive, either requiring repeated, costly model re-fits or…
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…
Anirban Chakraborty, Chloe Mattila, Debashis Ghosh, Brian Neelon + 1 more
High-throughput bulk and single-cell omics technologies enable comprehensive molecular profiling, yet identifying compact, biologically interpretable marker sets that distinguish cell types, conditions, or disease states remains challenging. Standard pipelines rely on univariate differential expression tests, which…
Klaus Oberauer, Philipp Musfeld, Frederik Aust
Bayes factors often require numerical estimation because closed-form solutions are unavailable. In six simulation studies, we explored the reliability, bias, and computational cost of two easy-to-use and broadly applicable methods: bridge sampling and the Savage-Dickey density ratios, based on Gaussian, logspline, and…
Authors not listed
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…
Authors not listed
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…
Jacob Thomas-Hegarty, Stefan R. Pulver, V Anne Smith
Neural information flow describes the movement of activity between neurons or brain areas. Advances in experimental methods have allowed production of large amounts of observational data related to neuronal activity from the single-neuron to population level. Most current methods for analysing these data are based on…
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…
Rachel Giblon, Chengyang Gao, Kuan Liu, Yongdong Ouyang + 4 more
Background Adaptive clinical trials increasingly aim to detect heterogeneity of treatment effect (HTE) to guide personalized care. However, most adaptive designs rely on predefined subgroups and are limited in their ability to uncover unknown or complex sources of HTE. Bayesian statistical methods offer a flexible…
Pär Villner, Matteo Bottai
Meta-analysis consists of a wide range of methods for summarizing existing research, often by aggregating summary statistics. The dominant methods are the fixed effect and the random effects models, which assume that all studies included in a meta-analysis are similar. In many scenarios, the available studies differ in…
Authors not listed
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…
Authors not listed
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…
Santiago Herce Castañon, Christopher R. Stephens
Predicting and understanding behaviour is a primary objective of many disciplines, especially human behaviour, as it is the cause of many of the world’s most pressing problems. Although it is a fundamental concept in multiple disciplines, there is no agreed operational definition of what it is. Neither is there a…
Chuanhai Liu
A central challenge in scientific inference is to produce uncertainty assessments that are both situation-specific and frequency-calibrated. This article examines inferential models (IMs) as a framework for prior-free probabilistic reasoning with scientific uncertainty. The central IM idea is to view the auxiliary…
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…
Authors not listed
Bonkowski and De Souza [Sol. Stat. Ionics 429, 116967 (2025)] provide a guide for performing molecular dynamics simulations of ion transport, including methods for estimating diffusion coefficients and their uncertainties from mean-squared displacement (MSD) data. The discussion of uncertainty in estimated diffusion…
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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…