Oxford Index Search Results

You are looking at 1-20 of 43 items for:

Bayes x Biomathematics and Statistics x clear all

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Overview page. Subjects: Architecture.

[Ge]

Structural division in the length of a building or roof. The unit within a building between a pair of piers or buttresses; the division of a roof marked by its main trusses.

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See overview in Oxford Index

Bayes’ Theorem

Therese M. Donovan and Ruth M. Mickey.

in Bayesian Statistics for Beginners

May 2019; p ublished online July 2019 .

Chapter. Subjects: Biomathematics and Statistics. 2622 words.

This chapter focuses on Bayes’ Theorem. The chapter first gives a brief introduction to Thomas Bayes, who first formulated the theorem. It then builds on the content presented in Chapters 1...

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Bayes and empirical Bayes: do they merge?

S. Petrone, J. Rousseau and C. Scricciolo.

in Biometrika

June 2014; p ublished online April 2014 .

Journal Article. Subjects: Biomathematics and Statistics; Probability and Statistics. 0 words.

Bayesian inference is attractive due to its internal coherence and for often having good frequentist properties. However, eliciting an honest prior may be difficult, and common practice is...

Nonparametric Bayes inference on conditional independence

Tsuyoshi Kunihama and David B. Dunson.

in Biometrika

March 2016; p ublished online January 2016 .

Journal Article. Subjects: Biomathematics and Statistics; Probability and Statistics. 6773 words.

In many application areas, a primary focus is on assessing evidence in the data refuting the assumption of independence of [math] and [math] conditionally on [math], with [math] response...

Optimal Bayes classifiers for functional data and density ratios

Xiongtao Dai, Hans-Georg Müller and Fang Yao.

in Biometrika

September 2017; p ublished online May 2017 .

Journal Article. Subjects: Biomathematics and Statistics; Probability and Statistics. 7731 words.

Summary

Bayes classifiers for functional data pose a challenge. One difficulty is that probability density functions do not exist for functional data, so the...

High-dimensional classification via nonparametric empirical Bayes and maximum likelihood inference

Lee H. Dicker and Sihai D. Zhao.

in Biometrika

March 2016; p ublished online February 2016 .

Journal Article. Subjects: Biomathematics and Statistics; Probability and Statistics. 7726 words.

We propose new nonparametric empirical Bayes methods for high-dimensional classification. Our classifiers are designed to approximate the Bayes classifier in a hypothesized hierarchical...

Empirical Bayes deconvolution estimates

Bradley Efron.

in Biometrika

March 2016; p ublished online February 2016 .

Journal Article. Subjects: Biomathematics and Statistics; Probability and Statistics. 8445 words.

An unknown prior density [math] has yielded realizations [math]. They are unobservable, but each [math] produces an observable value [math] according to a known probability mechanism, such...

The application of naive Bayes model averaging to predict Alzheimer's disease from genome-wide data

Wei Wei, Shyam Visweswaran and Gregory F Cooper.

in Journal of the American Medical Informatics Association

July 2011; p ublished online July 2011 .

Journal Article. Subjects: Medical Statistics and Methodology; Bioinformatics and Computational Biology; Biomathematics and Statistics. 5000 words.

Abstract

Objective Predicting patient outcomes from genome-wide measurements holds significant promise for improving clinical care. The large...

Nonparametric Bayes dynamic modelling of relational data

Daniele Durante and David B. Dunson.

in Biometrika

December 2014; p ublished online October 2014 .

Journal Article. Subjects: Biomathematics and Statistics; Probability and Statistics. 0 words.

Symmetric binary matrices representing relations are collected in many areas. Our focus is on dynamically evolving binary relational matrices, with interest being on inference on the...

An empirical Bayes approach for multiple tissue eQTL analysis

Gen Li, Andrey A Shabalin, Ivan Rusyn, Fred A Wright and Andrew B Nobel.

in Biostatistics

July 2018; p ublished online September 2017 .

Journal Article. Subjects: Biomathematics and Statistics; Probability and Statistics. 8184 words.

SUMMARY

Expression quantitative trait locus (eQTL) analyses identify genetic markers associated with the expression of a gene. Most up-to-date eQTL studies...

An empirical Bayes test for allelic-imbalance detection in ChIP-seq

Qi Zhang and Sündüz Keleş.

in Biostatistics

October 2018; p ublished online November 2017 .

Journal Article. Subjects: Biomathematics and Statistics; Probability and Statistics. 7637 words.

SUMMARY

Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) has enabled discovery of genomic regions enriched with biological...

Direct comparison between support vector machine and multinomial naive Bayes algorithms for medical abstract classification

Stan Matwin and Vera Sazonova.

in Journal of the American Medical Informatics Association

September 2012; p ublished online June 2012 .

Journal Article. Subjects: Medical Statistics and Methodology; Bioinformatics and Computational Biology; Biomathematics and Statistics. 870 words.

Bayesian Inference

Therese M. Donovan and Ruth M. Mickey.

in Bayesian Statistics for Beginners

May 2019; p ublished online July 2019 .

Chapter. Subjects: Biomathematics and Statistics. 3860 words.

Chapter 4 introduces the concept of Bayesian inference. The chapter discusses the scientific method, and illustrates how Bayes’ Theorem can be used for scientific inference. Bayesian...

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The Lorax Problem: Introduction to Bayesian Networks

Therese M. Donovan and Ruth M. Mickey.

in Bayesian Statistics for Beginners

May 2019; p ublished online July 2019 .

Chapter. Subjects: Biomathematics and Statistics. 8418 words.

The “Lorax Problem” introduces Bayesian networks, another set of methods that makes use of Bayes’ Theorem. The ideas are first explained in terms of a small, standard example that explores...

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MCMC Diagnostic Approaches

Therese M. Donovan and Ruth M. Mickey.

in Bayesian Statistics for Beginners

May 2019; p ublished online July 2019 .

Chapter. Subjects: Biomathematics and Statistics. 4215 words.

The purpose of this chapter is to illustrate some of the things that can go wrong in Markov Chain Monte Carlo (MCMC) analysis and to introduce some diagnostic tools that help identify...

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The Portrait Problem: Bayesian Inference with Joint Likelihood

Therese M. Donovan and Ruth M. Mickey.

in Bayesian Statistics for Beginners

May 2019; p ublished online July 2019 .

Chapter. Subjects: Biomathematics and Statistics. 4110 words.

Chapter 7 discusses the “Portrait Problem,” which concerns the dispute about whether a portrait frequently associated with Thomas Bayes (and used, in fact, as the cover of this book!) is...

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The Survivor Problem: Simple Linear Regression with MCMC

Therese M. Donovan and Ruth M. Mickey.

in Bayesian Statistics for Beginners

May 2019; p ublished online July 2019 .

Chapter. Subjects: Biomathematics and Statistics. 12428 words.

While one of the most common uses of Bayes’ Theorem is in the statistical analysis of a dataset (i.e., statistical modeling), this chapter examines another application of Gibbs sampling:...

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The Once-ler Problem: Introduction to Decision Trees

Therese M. Donovan and Ruth M. Mickey.

in Bayesian Statistics for Beginners

May 2019; p ublished online July 2019 .

Chapter. Subjects: Biomathematics and Statistics. 4848 words.

In the “Once-ler Problem,” the decision tree is introduced as a very useful technique that can be used to answer a variety of questions and assist in making decisions. This chapter builds...

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Probability and random variables

M. D. Edge.

in Statistical Thinking from Scratch

June 2019; p ublished online October 2019 .

Chapter. Subjects: Biomathematics and Statistics. 12344 words.

This chapter considers the rules of probability. Probabilities are non-negative, they sum to one, and the probability that either of two mutually exclusive events occurs is the sum of the...

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Bayesian estimation and inference

M. D. Edge.

in Statistical Thinking from Scratch

June 2019; p ublished online October 2019 .

Chapter. Subjects: Biomathematics and Statistics. 9757 words.

Bayesian methods allow researchers to combine precise descriptions of prior beliefs with new data in a principled way. The main object of interest in Bayesian statistics is the posterior...

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Bayesian Statistics for Beginners

Therese Donovan and Ruth M. Mickey.

May 2019; p ublished online July 2019 .

Book. Subjects: Biomathematics and Statistics. 432 pages.

Bayesian Statistics for Beginners is an entry-level book on Bayesian statistics. It is like no other math book you’ve read. It is written for readers who do not have advanced...

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