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Posterior probability
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#	Item
1	Channel Polarization and Blackwell Measures Maxim Raginsky Abstract—The Blackwell measure of a binary-input channel (BIC) is the distribution of the posterior probability of 0 under the uniform input distribution. This Add to Reading List Source URL: maxim.ece.illinois.edu Language: English - Date: 2016-05-05 12:45:19
2	remote sensing Article Posterior Probability Modeling and Image Classification for Archaeological Site Prospection: Building a Survey Efficacy Model for Identifying Add to Reading List Source URL: www.cis.jhu.edu Language: English - Date: 2016-06-22 07:53:41
3	Reliable Posterior Probability Estimation for Streaming Face Recognition Abhijit Bendale University of Colorado at Colorado Springs Terrance Boult University of Colorado at Colorado Springs Add to Reading List Source URL: www.cv-foundation.org - Date: 2014-06-02 17:19:15
4	6090 JOURNAL OF CLIMATE VOLUME 23 Add to Reading List Source URL: myweb.fsu.edu Language: English - Date: 2011-06-17 16:27:14 Statistics Statistical theory Regression analysis Bayesian statistics Statistical models Econometrics Actuarial science Linear regression Generalized linear model Probability distribution Posterior probability Prior probability
5	Probabilistic ML algorithm Naïve Bayes and Maximum Likelyhood 1 Add to Reading List Source URL: twiki.di.uniroma1.it Language: English - Date: 2016-05-09 09:04:44 Probability theory Conditional probability Joint probability distribution Probability distribution Expected value Naive Bayes classifier Random variable Probability density function Probability Posterior probability Mutual information
6	output/maye11bayesian.dvi Add to Reading List Source URL: europa.informatik.uni-freiburg.de Language: English - Date: 2012-02-24 09:20:37 Bayesian statistics Probability distributions Mixture model Posterior predictive distribution Hidden Markov model Normal distribution Hyperparameter Prior probability Dirichlet distribution RT Image segmentation Dirichlet-multinomial distribution
7	16 Basic Bayesian Methods Mark E. Glickman and David A. van Dyk Summary In this chapter, we introduce the basics of Bayesian data analysis. The key ingredients to a Add to Reading List Source URL: www.glicko.net Language: English - Date: 2009-12-24 15:06:45 Bayesian statistics Bayesian inference Prior probability Beta distribution Likelihood function Statistical inference Normal distribution Confidence interval Posterior probability Conjugate prior Marginal likelihood
8	Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis Add to Reading List Source URL: dept.stat.lsa.umich.edu Language: English - Date: 2014-01-10 12:37:27 Statistics Probability Statistical natural language processing Probability distributions Latent Dirichlet allocation Topic model Mixture model Dirichlet distribution Normal distribution Gibbs sampling Statistical inference
9	Bayesian Analysis, Number 4, pp. 631–652 Hierarchical Bayesian Modeling of Hitting Performance in Baseball Add to Reading List Source URL: www-stat.wharton.upenn.edu Language: English - Date: 2009-11-30 14:51:49 Statistics Probability Mathematical analysis Probability distributions Dirichlet distribution Mixture model Normal distribution Constructible universe Posterior predictive distribution Markov chain Relationships among probability distributions Beta distribution
10	Power Weighted Densities for Time Series Data Add to Reading List Source URL: www-stat.wharton.upenn.edu Language: English - Date: 2016-03-28 14:59:23 Statistics Statistical theory Probability Estimation theory Bayesian statistics Probability distributions M-estimators Maximum likelihood estimation Linear regression Posterior predictive distribution Prior probability Likelihood function