26 papers · ranked by Valyu relevance
Tony Tohme, Kevin Vanslette, Kamal Youcef‐Toumi
In model development, model calibration and validation play complementary roles toward learning reliable models. In this article, we expand the Bayesian Validation Metric framework to a general calibration and validation framework by inverting the validation mathematics into a generalized Bayesian method for model…
Georg Heinze, Stefan Michiels, Martin Posch, Menelaos Pavlou + 4 more
'Gareth Ambler' 'Shaun Seaman' 'Maria De\xa0Iorio' 'Rumana Z Omar'] Risk prediction models are used to predict a clinical outcome for patients using a set of predictors. We focus on predicting low-dimensional binary outcomes typically arising in epidemiology, health services and public health research where logistic…
Alexey Miroshnikov, E. V. Savel'ev, Erin M. Conlon
BayesSummaryStatLM for Bayesian linear regression models with Markov chain Monte Carlo implementation that overcomes these limitations. Our Bayesian models use only summary statistics of data as input; these summary statistics can be calculated from subsets of big data and combined over subsets. Thus, complete data…
Don van den Bergh, Merlise A. Clyde, Akash R. Komarlu Narendra Gupta, Tim de Jong + 4 more
'Tim de Jong' 'Quentin F. Gronau' 'Maarten Marsman' 'Alexander Ly' 'Eric-Jan Wagenmakers'] Linear regression analyses commonly involve two consecutive stages of statistical inquiry. In the first stage, a single ‘best’ model is defined by a specific selection of relevant predictors; in the second stage, the regression…
Enes Makalic, Daniel F. Schmidt
Bayesian penalized regression techniques, such as the Bayesian lasso and the Bayesian horseshoe estimator, have recently received a significant amount of attention in the statistics literature. However, software implementing state-of-the-art Bayesian penalized regression, outside of general purpose Markov chain Monte…
George Karabatsos
Most of applied statistics involves regression analysis of data. In practice, it is important to specify a regression model that has minimal assumptions which are not violated by data, to ensure that statistical inferences from the model are informative and not misleading. This paper presents a stand-alone and…
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…
Eric Schulz, Maarten Speekenbrink, Andreas Krause
This tutorial introduces the reader to Gaussian process regression as a tool to model, actively explore and exploit unknown functions. Gaussian process regression is a powerful, non-parametric Bayesian approach towards regression problems that can be utilized in exploration and exploitation scenarios. This tutorial…
Themistoklis Botsas, Lachlan Mason, Indranil Pan
We introduce a novel rule-based approach for handling regression problems. The new methodology carries elements from two frameworks: (i) it provides information about the uncertainty of the parameters of interest using Bayesian inference, and (ii) it allows the incorporation of expert knowledge through rule-based…
Teddy Groves, Nicholas Luke Cowie, Lars Keld Nielsen
Facilitates Quantitative Modeling of Cell Metabolism Authors: ['Teddy Groves' 'Nicholas Luke Cowie' 'Lars Keld Nielsen'] This paper presents Maud, a command-line application that implements Bayesian statistical inference for kinetic models of biochemical metabolic reaction networks. Maud takes into account quantitative…
Natalia Levshina
In recent years, Bayesian statistics has gained traction across a wide range of scientific disciplines. This paper explores the growing application of Bayesian methods within the field of linguistics and considers their future potential. A survey of articles from different linguistics journals indicates that Bayesian…
Niu Du, Yang Chen, Yu Qian
Curve fitting is a key statistical method in the pharmaceutical industry for modeling the relationship between drug effects and dose levels. Traditional regression-based curve fitting is computationally efficient but sensitive to technical errors, particularly with small sample sizes. In practice, budget constraints…
Ernest Fokoué
This paper seeks to provide a thorough account of the ubiquitous nature of the Bayesian paradigm in modern statistics, data science and artificial intelligence. Once maligned, on the one hand by those who philosophically hated the very idea of subjective probability used in prior specification, and on the other hand…
Md Tanwir Akhtar, Athar Ali Khan
Log-Burr distribution is a generalization of logistic and extreme value distributions, which are important reliability models. In this paper, Bayesian approach is used to model reliability data for log-Burr model using analytic and simulation tools. Laplace approximation is implemented for approximating posterior…
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…
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…
Arthur Newbury
Estimating underlying cooccurrence relationships between pairs of species has long been a challenging task in ecology as the extent to which species actually cooccur is partially dependent on their prevalences. While recent work has taken large steps towards solving this problem, the next question is how to assess the…
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…
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…
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…
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…
Authors not listed
Plastic mechanical recycling is the conventional technological step towards circularity. In such aspects, complex mixtures of polyolefin blends are often fed into mechanical recycling systems, resulting in moulded products with uncertain quality. To add to the difficulty of heterogeneous feedstocks, the testing of…
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…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? This type of solution degeneracy often exists in physics-based simulations and wet-lab experiments, but constraining these degeneracies is often unsupported or difficult to implement in many optimization packages, requiring additional time and…
Riley Hickman, Matteo Aldeghi, Alán Aspuru-Guzik
Model-based optimization strategies, such as Bayesian optimization (BO), have been deployed across the natural sciences in design and discovery campaigns due to their sample efficiency and flexibility. The combination of such strategies with automated laboratory equipment and/or high-performance computing in a…
Kan Hatakeyama-Sato, Seigo Watanabe, Naoki Yamane, Yasuhiko Igarashi + 1 more
Materials informatics and cheminformatics struggle with data scarcity, hindering the extraction of significant relationships between structures and properties. The "Ugly Duckling" theorem, suggesting the difficulty of data processing without assumptions or prior knowledge, exacerbates this problem. Current…