Comparing and Combining Generative and Posterior Probability Models:P = posterior(obj,X) returns the posterior probabilities of each of the k components in the Gaussian mixture ... Cluster Analysis · Gaussian Mixture Models

How to compute posterior model probabilities ... and why ...

www.math.rug.nl/stat/models/files/green.pdf

University of Groningen

Mar 15, 2011 - the product of posterior model probabilities and model-specific parameter posteriors. – very often the basis for reporting the inference, and in ...

[PDF]Posterior probability

https://astro.uni-bonn.de/.../Lecture3_2012.pdf

Hoher List Observatory

model parameters but it is not a probability density for θ). P(θ|x): old name “inverse probability” modern name “posterior probability”. Starting from observed ...

[PDF]a comparison of the information and posterior probability ...

https://www.princeton.edu/~erp/.../M253.pdf

Princeton University

by GC Chow - ‎1979 - ‎Cited by 102 - ‎Related articles

POSTERIOR PROBABILITY CRITERIA FOR MODEL SELECTION. Gregory C. ... formodel selection based on the posterior probability criterion, and points out.

What are posterior probabilities and prior probabilities ...

support.minitab.com/.../modeling.../what-are-posterior-and-prior-probab...

A posterior probability is the probability of assigning observations to groups given the data. A prior probability is the probability that an observation will fall into a ...

[PDF]Posterior Model Probabilities via Path-based Pairwise Priors ...

https://www2.stat.duke.edu/~berger/papers/pathwise.pdf

Duke University

by JO Berger - ‎Cited by 41 - ‎Related articles

Posterior Model Probabilities via Path-based. Pairwise Priors. James O. Berger1. Duke University and Statistical and Applied Mathematical Sciences Institute,.

Bayesian model choice based on Monte Carlo estimates of ...

www.sciencedirect.com/science/.../S0167947304002464

ScienceDirect

by P Congdon - ‎2006 - ‎Cited by 56 - ‎Related articles

An approach is outlined here that produces posterior model probabilities and hence Bayes factor estimates but not marginal likelihoods. It uses a Monte Carlo ...

Posterior probabilities for choosing a regression model

www.jstor.org/stable/2335274

JSTOR

by AC Atkinson - ‎1978 - ‎Cited by 142 - ‎Related articles

Biometrika (1978), 65, 1, pp. 39-48. 39. With 4 text-figures. Printed in Great Britain.Posterior probabilities for choosing a regression model. BY A. C. ATKINSON.

Posterior probabilities of components - MATLAB - MathWorks

www.mathworks.com › ... › Gaussian Mixture Models

MathWorks

P = posterior(obj,X) returns the posterior probabilities of each of the k components in the Gaussian mixture ... Cluster Analysis · Gaussian Mixture Models.

[PDF]Comparing and Combining Generative and Posterior ...

www.aclweb.org/.../W04-3209...

Association for Computational Linguistics

by Y Liu - ‎Cited by 35 - ‎Related articles

Comparing and Combining Generative and Posterior Probability Models: Some Advances in Sentence Boundary Detection in Speech. Yang Liu. ICSI and .