15 papers · ranked by Valyu relevance
Yui Hayashi, Shun Katakami, Shigeo Kuwamoto, Kenji Nagata + 3 more
'Masaichiro Mizumaki' 'Masato Okada' 'J. Ilavsky'] A Bayesian method is proposed for quantitatively selecting a mathematical model of a sample for small-angle scattering. The performance of this method is evaluated through numerical experiments on artificial data for a sample containing a mixture of multiple spherical…
Eli N. Weinstein, Jeffrey W. Miller
Insights into complex, high-dimensional data can be obtained by discovering features of the data that match or do not match a model of interest. To formalize this task, we introduce the "data selection" problem: finding a lower-dimensional statistic-such as a subset of variables-that is well fit by a given parametric…
Sierra A. Bainter, Thomas G. McCauley, Mahmoud M. Fahmy, Zachary T. Goodman + 2 more
'Zachary T. Goodman' 'Lauren B. Kupis' 'J. Sunil Rao'] In the current paper, we review existing tools for solving variable selection problems in psychology. Modern regularization methods such as lasso regression have recently been introduced in the field and are incorporated into popular methodologies, such as network…
Bart van Erp, Wouter W. L. Nuijten, Thijs van de Laar, Bert de Vries + 1 more
'Patrick Shafto'] Bayesian state and parameter estimation are automated effectively in a variety of probabilistic programming languages. The process of model comparison on the other hand, which still requires error-prone and time-consuming manual derivations, is often overlooked despite its importance. This paper…
Kaixin Yang, Long Liu, Yalu Wen
Feature selection is an indispensable step for the analysis of high-dimensional molecular data. Despite its importance, consensus is lacking on how to choose the most appropriate feature selection methods, especially when the performance of the feature selection methods itself depends on hyper-parameters. Bayesian…
Shouhao Zhou, Udo Von Toussaint
We propose a new model selection method, named the posterior averaging information criterion, for Bayesian model assessment to minimize the risk of predicting independent future observations. The theoretical foundation is built on the Kullback-Leibler divergence to quantify the similarity between the proposed candidate…
Nicolas Lartillot, Sebastian Hohna
There is still no consensus as to how to select models in Bayesian phylogenetics, and more generally in applied Bayesian statistics. Bayes factors are often presented as the method of choice, yet other approaches have been proposed, such as cross-validation or information criteria. Each of these paradigms raises…
Mohammadreza Shiri, Sajjad Moharramnejad, Afshar Estakhr, Sharareh Fareghi + 5 more
'Sharareh Fareghi' 'Hamid Najafinezhad' 'Saeed Khavari Khorasani' 'Aziz Afarinesh' 'Morteza Eshraghi-Nejad' 'Vignesh Muthusamy'] Plant breeders are increasingly utilizing stability parameters as valuable tools for selecting cultivars in the context of genotype × environment interaction (GEI). Neglecting GEI in…
Mark A. Gannon
Students are told in basic probability classes that there are two main “schools” of statistics, the frequentist and the Bayesian, and that those different views of how to approach statistical inference problems arise from two different views of the meaning of probability. Practicing scientists know things are not that…
Julien Diot, Hiroyoshi Iwata
Introduction Advances in genotyping technologies have provided breeders with access to the genotypic values of several thousand genetic markers in their breeding materials. Combined with phenotypic data, this information facilitates genomic selection. Although genomic selection can benefit breeders, it does not…
Prasanta S. Bandyopadhyay, Samidha Shetty, Gordon Brittan Jr., Geert Verdoolaege
An extensive literature on decision theory has been developed by both subjective Bayesians and Neyman-Pearson (NP) theorists, with more recent contributions to it from evidential decision theorists. The last-mentioned, however, have often been framed from a Bayesian perspective and therefore retain a subjectivist…
Matthew Stephens
I discuss the benefits of looking through the ‘Bayesian lens’ (seeking a Bayesian interpretation of ostensibly non-Bayesian methods), and the dangers of wearing ‘Bayesian blinkers’ (eschewing non-Bayesian methods as a matter of philosophical principle). I hope that the ideas may be useful to scientists trying to…
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…
Hannah Fenwick, Guillermo Campitelli, Matthew B. Thompson
Research on Bayesian reasoning has been shaped by two productive traditions: ecological rationality, which explains why natural frequencies facilitate inference, and nested sets accounts, which show how transparent set relations support analytic reasoning. Together, these approaches have generated a rich empirical…
Lucy Cui, Stephanie Lo, Zili Liu
Decisions are often made under uncertainty. The most that one can do is use prior knowledge (e.g., base rates, prior probabilities, etc.) and make the most probable choice given the information we have. Unfortunately, most people struggle with Bayesian reasoning. Poor performance within Bayesian reasoning problems has…