Posterior predictive distribution

Results: 6



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Predictive probabilities for normal outcomes John Cook September 15, 2011 Suppose Y ∼ normal(θ, σ 2 ) and a priori θ ∼ normal(µ, τ ). After observing y1 , y2 , . . . , yn the posterior distribution on θ is norm

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Source URL: www.johndcook.com

Language: English - Date: 2013-07-09 18:22:24
    2Atlantic hurricane seasons / Atlantic hurricane / Tropical cyclone / Hurricane Andrew / Posterior predictive distribution

    VOLUME 14 JOURNAL OF CLIMATE 1 DECEMBER 2001

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    Source URL: myweb.fsu.edu

    Language: English - Date: 2008-10-19 22:20:40
    3Bayesian statistics / Probability distributions / Mixture model / Posterior predictive distribution / Hidden Markov model / Normal distribution / Hyperparameter / Prior probability / Dirichlet distribution / RT / Image segmentation / Dirichlet-multinomial distribution

    output/maye11bayesian.dvi

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    Source URL: europa.informatik.uni-freiburg.de

    Language: English - Date: 2012-02-24 09:20:37
    4Statistics / Probability / Mathematical analysis / Probability distributions / Dirichlet distribution / Mixture model / Normal distribution / Constructible universe / Posterior predictive distribution / Markov chain / Relationships among probability distributions / Beta distribution

    Bayesian Analysis, Number 4, pp. 631–652 Hierarchical Bayesian Modeling of Hitting Performance in Baseball

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    Source URL: www-stat.wharton.upenn.edu

    Language: English - Date: 2009-11-30 14:51:49
    5Statistics / Statistical theory / Probability / Estimation theory / Bayesian statistics / Probability distributions / M-estimators / Maximum likelihood estimation / Linear regression / Posterior predictive distribution / Prior probability / Likelihood function

    Power Weighted Densities for Time Series Data

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    Source URL: www-stat.wharton.upenn.edu

    Language: English - Date: 2016-03-28 14:59:23
    6Estimation theory / Statistical inference / Allan variance / Statistics / Normal distribution / Variance

    Predictive probabilities for normal outcomes John Cook September 15, 2011 Suppose Y ∼ normal(θ, σ 2 ) and a priori θ ∼ normal(µ, τ ). After observing y1 , y2 , . . . , yn the posterior distribution on θ is norm

    Add to Reading List

    Source URL: www.johndcook.com

    Language: English - Date: 2013-07-09 18:22:24
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