﻿ "Posterior Distribution" related terms, short phrases and links

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Posterior Distribution       Article     History   Tree Map
 Encyclopedia of Keywords > Science > Mathematics > Statistics > Bayesian Statistics > Posterior Distribution Michael Charnine

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### DefinitionsUpDw('Definitions','-Abz-');

1. The posterior distribution is fine, and an extreme p-value would be inappropriate. (Web site)

### OutputUpDw('OUTPUT','-Abz-');

1. You can output the posterior distribution to a SAS data set for use in additional analysis.

### ResultUpDw('RESULT','-Abz-');

1. In contrast, the result of Bayesian training is a posterior distribution over network weights. (Web site)
2. The result of such analysis is the posterior distribution of an intensity function with covariate effects.

### SamplesUpDw('SAMPLES','-Abz-');

1. Under broad conditions this process eventually provides samples from the joint posterior distribution of the unknown quantities.
2. We use the reversible jump MCMC method of G REEN 1995 to generate samples from the joint posterior distribution. (Web site)

### ModelUpDw('MODEL','-Abz-');

1. After the model had converged, samples from the conditional distributions were used to summarize the posterior distribution of the model.

### Prior DistributionUpDw('PRIOR_DISTRIBUTION','-Abz-');

1. Let λ be a prior distribution on Θ and let be the posterior distribution for the sample size n given.

### ProblemUpDw('PROBLEM','-Abz-');

1. This need not be a problem if the posterior distribution is proper. (Web site)

### ComputingUpDw('COMPUTING','-Abz-');

1. If we were computing a posterior distribution, that could be approximated by a normal distribution with mean 5.45 and variance 0.04541667.
2. This process of computing the posterior distribution of variables given evidence is called probabilistic inference. (Web site)

### SampleUpDw('SAMPLE','-Abz-');

1. In this approach, the posterior distribution is represented by a sample of perhaps a few dozen sets of network weights. (Web site)

### WeightsUpDw('WEIGHTS','-Abz-');

1. The sample is obtained by simulating a Markov chain whose equilibrium distribution is the posterior distribution for weights and hyperparameters. (Web site)

### InferenceUpDw('INFERENCE','-Abz-');

1. Clarke B., On the overall sensitivity of the posterior distribution to its inputs, J. Statistical Planning and Inference, 71, 1998, 137-150. (Web site)

### DataUpDw('DATA','-Abz-');

1. After we look at the data (or after our program looks at the data), our revised opinions are captured by a posterior distribution over network weights. (Web site)

### PriorUpDw('PRIOR','-Abz-');

1. A conjugate prior is one which, when combined with the likelihood and normalised, produces a posterior distribution which is of the same type as the prior. (Web site)
2. Here, the idea is to maximize the expected Kullback-Leibler divergence of the posterior distribution relative to the prior.

### P-ValueUpDw('P-VALUE','-Abz-');

1. Moreover one can get plausible information about the posterior distribution of the parameters from mcmcsamp(), even a Bayesian equivalent of a p-value. (Web site)

### P-ValuesUpDw('P-VALUES','-Abz-');

1. There is an argument about p-values per se, One should, arguably, be examining the profile likelihood, or the whole of the posterior distribution. (Web site)

### NumberUpDw('NUMBER','-Abz-');

1. Figure 4. Posterior distribution of the number of QTL for real data. (Web site)

### UnknownsUpDw('UNKNOWNS','-Abz-');

1. The MCMC method produces a posterior distribution over the unknowns in their model.
2. We employ a Bayesian framework in which statistical inference is based on the joint posterior distribution of all unknowns. (Web site)

### Bayesian AnalysisUpDw('BAYESIAN_ANALYSIS','-Abz-');

1. Bayesian analysis generally requires a computer-intensive approach to estimate the posterior distribution. (Web site)

### Maximum Likelihood EstimateUpDw('MAXIMUM_LIKELIHOOD_ESTIMATE','-Abz-');

1. As shown by Schwartz (1966), the posterior distribution may behave well even when the maximum likelihood estimate does not.

### Random VariableUpDw('RANDOM_VARIABLE','-Abz-');

1. Using the reversible jump MCMC method, we can treat the number of QTL as a random variable and generate its posterior distribution. (Web site)

### Bayesian StatisticsUpDw('BAYESIAN_STATISTICS','-Abz-');

1. In Bayesian statistics, a maximum a posteriori probability (MAP) estimate is a mode of the posterior distribution.

### Posterior DistributionUpDw('POSTERIOR_DISTRIBUTION','-Abz-');

1. In Bayesian statistics a prior distribution is multiplied by a likelihood function and then normalised to produce a posterior distribution.
2. The sequential use of the Bayes' formula: when more data becomes available after calculating a posterior distribution, the posterior becomes the next prior. (Web site)
3. Given a prior probability distribution for one or more parameters, sample evidence can be used to generate an updated posterior distribution.

### CategoriesUpDw('Categories','-Abz-');

1. Science > Mathematics > Statistics > Bayesian Statistics
2. Prior Distribution
3. Qtl
4. Maximum Likelihood Estimate
5. Bayesian Analysis
6. Books about "Posterior Distribution" in Amazon.com
 Short phrases about "Posterior Distribution"   Originally created: April 04, 2011.   Links checked: February 27, 2013.   Please send us comments and questions by this Online Form   Please click on to move good phrases up.
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