17 papers · ranked by Valyu relevance
Karl Friston, Will Penny
This note describes a Bayesian model selection or optimization procedure for post hoc inferences about reduced versions of a full model. The scheme provides the evidence (marginal likelihood) for any reduced model as a function of the posterior density over the parameters of the full model. It rests upon specifying…
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…
Miguel A. Negrín, Francisco J. Vázquez-Polo, María Martel, Elías Moreno + 1 more
'Elías Moreno' 'Francisco J. Girón'] Linear regression models are often used to represent the cost and effectiveness of medical treatment. The covariates used may include sociodemographic variables, such as age, gender or race; clinical variables, such as initial health status, years of treatment or the existence of…
Soonil Kwon, Dai Wang, Xiuqing Guo
Genome-wide association studies usually involve several hundred thousand of single-nucleotide polymorphisms (SNPs). Conventional approaches face challenges when there are enormous number of SNPs but a relatively small number of samples and, in some cases, are not feasible. We introduce here an iterative Bayesian…
Ang Li, Luis Pericchi, Kun Wang
There is not much literature on objective Bayesian analysis for binary classification problems, especially for intrinsic prior related methods. On the other hand, variational inference methods have been employed to solve classification problems using probit regression and logistic regression with normal priors. In this…
Takeshi Hayashi, Hiroyoshi Iwata
Background In genomic selection, a model for prediction of genome-wide breeding value (GBV) is constructed by estimating a large number of SNP effects that are included in a model. Two Bayesian methods based on MCMC algorithm, Bayesian shrinkage regression (BSR) method and stochastic search variable selection (SSVS)…
Yohei Murakami, Maria Anisimova
Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection. Especially, the framework named approximate Bayesian computation is often used for parameter inference and model selection in…
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…
Andrea Murari, Emmanuele Peluso, Francesco Cianfrani, Pasquale Gaudio + 1 more
'Pasquale Gaudio' 'Michele Lungaroni'] The most widely used forms of model selection criteria, the Bayesian Information Criterion (BIC) and the Akaike Information Criterion (AIC), are expressed in terms of synthetic indicators of the residual distribution: the variance and the mean-squared error of the residuals…
Flavia Alves da Silva, Alexandre Pio Viana, Caio Cezar Guedes Correa, Eileen Azevedo Santos + 4 more
'Eileen Azevedo Santos' 'Julie Anne Vieira Salgado de Oliveira' 'José Daniel Gomes Andrade' 'Rodrigo Moreira Ribeiro' 'Leonardo Siqueira Glória'] Markers are an important tool in plant breeding, which can improve conventional phenotypic breeding, generating more accurate information outcoming better decision making.…
John O. Campbell
Many of the mathematical frameworks describing natural selection are equivalent to Bayes' Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian…
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…
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…
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…
Rens van de Schoot, Naomi Schalken, Miranda Olff
2.1.### Flexible hypothesis testing The first reason why Bayesian statistics is used is that it provides a flexible alternative to null hypothesis significance testing. van Veen, Engelhard, and van den Hout ([CIT0036]) tested several predictions from working memory theory that could explain the efficacy of eye movement…
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…