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

Keywords and Sections
OUTPUT
RESULT
SAMPLES
MODEL
PRIOR DISTRIBUTION
PROBLEM
COMPUTING
SAMPLE
WEIGHTS
INFERENCE
DATA
PRIOR
P-VALUE
P-VALUES
NUMBER
UNKNOWNS
BAYESIAN ANALYSIS
MAXIMUM LIKELIHOOD ESTIMATE
RANDOM VARIABLE
BAYESIAN STATISTICS
POSTERIOR DISTRIBUTION
Review of Short Phrases and Links

    This Review contains major "Posterior Distribution"- related terms, short phrases and links grouped together in the form of Encyclopedia article.

Definitions

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

Output

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

Result

  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.

Samples

  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)

Model

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

Prior Distribution

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

Problem

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

Computing

  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)

Sample

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

Weights

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

Inference

  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)

Data

  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)

Prior

  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-Value

  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-Values

  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)

Number

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

Unknowns

  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 Analysis

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

Maximum Likelihood Estimate

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

Random Variable

  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 Statistics

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

Posterior Distribution

  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.

Categories

  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

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  Short phrases about "Posterior Distribution"
  Originally created: April 04, 2011.
  Links checked: February 27, 2013.
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