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## Parsimony and Bayesian phylogenetics

*Pablo A. Goloboff and Diego Pol*

### in Parsimony, Phylogeny, and Genomics

- Published in print:
- 2006
- Published Online:
- September 2007
- ISBN:
- 9780199297306
- eISBN:
- 9780191713729
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199297306.003.0008
- Subject:
- Biology, Evolutionary Biology / Genetics

The intent of a statistically-based phylogenetic method is to estimate tree topologies and values of possibly relevant parameters, as well as the uncertainty inherent in those estimations. A method ... More

## Keeping Up to Date

*Ken Binmore*

### in Playing for Real: Game Theory

- Published in print:
- 2007
- Published Online:
- May 2007
- ISBN:
- 9780195300574
- eISBN:
- 9780199783748
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195300574.003.0013
- Subject:
- Economics and Finance, Microeconomics

This chapter is about Bayesian decision theory. It explains why game theorists model players' beliefs using subjective probability distributions, and how these beliefs are updated using Bayes' rule ... More

## A Negative Answer

*Erik J. Olsson*

### in Against Coherence: Truth, Probability, and Justification

- Published in print:
- 2005
- Published Online:
- July 2005
- ISBN:
- 9780199279999
- eISBN:
- 9780191602665
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0199279993.003.0007
- Subject:
- Philosophy, Metaphysics/Epistemology

A sustained argument is given showing that more coherence does not imply a higher likelihood of truth even in fortunate circumstances (independence, individual credibility) and in a ceteris paribus ... More

## Bayesian Basics and the Scientific Hypothesis

*Bradley E. Alger*

### in Defense of the Scientific Hypothesis: From Reproducibility Crisis to Big Data

- Published in print:
- 2019
- Published Online:
- February 2021
- ISBN:
- 9780190881481
- eISBN:
- 9780190093761
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780190881481.003.0006
- Subject:
- Neuroscience, Techniques

This chapter covers the basics of Bayesian statistics, emphasizing the conceptual framework for Bayes’ Theorem. It works through several iterations of the theorem to demonstrate how the same equation ... More

## The Author Problem: Bayesian Inference with Two Hypotheses

*Therese M. Donovan and Ruth M. Mickey*

### in Bayesian Statistics for Beginners: a step-by-step approach

- Published in print:
- 2019
- Published Online:
- July 2019
- ISBN:
- 9780198841296
- eISBN:
- 9780191876820
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780198841296.003.0005
- Subject:
- Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies

The “Author Problem” provides a concrete example of Bayesian inference. This chapter draws on work by Frederick Mosteller and David Wallace, who used Bayesian inference to assign authorship for ... More

## Bayesian Treatments of Neuroimaging Data

*Will Penny and Karl Friston*

### in Bayesian Brain: Probabilistic Approaches to Neural Coding

- Published in print:
- 2006
- Published Online:
- August 2013
- ISBN:
- 9780262042383
- eISBN:
- 9780262294188
- Item type:
- chapter

- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262042383.003.0005
- Subject:
- Neuroscience, Disorders of the Nervous System

This chapter describes the application of Bayesian methods to neuroimaging data. First, it introduces a functional magnetic resonance imaging (fMRI) data set that is analysed using posterior ... More

## Entropy Regularization

*Grandvalet Yves and Bengio Yoshua*

### in Semi-Supervised Learning

- Published in print:
- 2006
- Published Online:
- August 2013
- ISBN:
- 9780262033589
- eISBN:
- 9780262255899
- Item type:
- chapter

- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262033589.003.0009
- Subject:
- Computer Science, Machine Learning

This chapter promotes the use of entropy regularization as a means to benefit from unlabeled data in the framework of maximum a posteriori estimation. The learning criterion is derived from clearly ... More

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