bayesiancook.blogspot.com

The Bayesian kitchen

Statistical inference and evolutionary biology. Thursday, November 13, 2014. Bayesian diversification models (2). Concerning my last post, I realize that my notation for rejection sampling models is a bit elliptic. In particular, the rejection step corresponding to the fact that we condition on the observed data is never explicitly written. So, let me just reformulate the technical arguments and restate the main idea. Sample $ theta$ from prior. Simulate data given $ theta$. UNTIL simulated phylogeny mat...

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The Bayesian kitchen | bayesiancook.blogspot.com Reviews

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Statistical inference and evolutionary biology. Thursday, November 13, 2014. Bayesian diversification models (2). Concerning my last post, I realize that my notation for rejection sampling models is a bit elliptic. In particular, the rejection step corresponding to the fact that we condition on the observed data is never explicitly written. So, let me just reformulate the technical arguments and restate the main idea. Sample $ theta$ from prior. Simulate data given $ theta$. UNTIL simulated phylogeny mat...

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4 return $ theta$

5 until process survives

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The Bayesian kitchen | bayesiancook.blogspot.com Reviews

https://bayesiancook.blogspot.com

Statistical inference and evolutionary biology. Thursday, November 13, 2014. Bayesian diversification models (2). Concerning my last post, I realize that my notation for rejection sampling models is a bit elliptic. In particular, the rejection step corresponding to the fact that we condition on the observed data is never explicitly written. So, let me just reformulate the technical arguments and restate the main idea. Sample $ theta$ from prior. Simulate data given $ theta$. UNTIL simulated phylogeny mat...

INTERNAL PAGES

bayesiancook.blogspot.com

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The Bayesian kitchen: December 2013

http://www.bayesiancook.blogspot.com/2013_12_01_archive.html

Statistical inference and evolutionary biology. Tuesday, December 31, 2013. Can we, under certain conditions, interpret posterior probabilities in frequentist terms? That is, such that, under controlled simulation settings, 95% of our 95% credible intervals across our probabilistic evaluations turn out to contain the true value, or that 95% of the clades that are inferred to be monophyletic with a posterior probability of 0.95 turn out to be indeed monophyletic? Of course, if. Note that there are often s...

2

The Bayesian kitchen: Algorithmic models of probabilistic inference

http://www.bayesiancook.blogspot.com/2014/10/algorithmic-models-of-probabilistic.html

Statistical inference and evolutionary biology. Friday, October 31, 2014. Algorithmic models of probabilistic inference. Rejection sampling is clearly not the most efficient Monte Carlo estimator. Even so, it is a conceptually beautiful idea: fundamentally, rejection sampling represents an. The algorithm works as follows. Given some observed data $d$ and a model parameterized by $ theta$:. Randomly choose parameter $ theta$ from the prior. Simulate data $D$, using $ theta$ as your parameter vector. Find ...

3

The Bayesian kitchen: November 2014

http://www.bayesiancook.blogspot.com/2014_11_01_archive.html

Statistical inference and evolutionary biology. Thursday, November 13, 2014. Bayesian diversification models (2). Concerning my last post, I realize that my notation for rejection sampling models is a bit elliptic. In particular, the rejection step corresponding to the fact that we condition on the observed data is never explicitly written. So, let me just reformulate the technical arguments and restate the main idea. Sample $ theta$ from prior. Simulate data given $ theta$. UNTIL simulated phylogeny mat...

4

The Bayesian kitchen: Bayesian diversification models (2)

http://www.bayesiancook.blogspot.com/2014/11/bayesian-diversification-models-2.html

Statistical inference and evolutionary biology. Thursday, November 13, 2014. Bayesian diversification models (2). Concerning my last post, I realize that my notation for rejection sampling models is a bit elliptic. In particular, the rejection step corresponding to the fact that we condition on the observed data is never explicitly written. So, let me just reformulate the technical arguments and restate the main idea. Sample $ theta$ from prior. Simulate data given $ theta$. UNTIL simulated phylogeny mat...

5

The Bayesian kitchen: January 2014

http://www.bayesiancook.blogspot.com/2014_01_01_archive.html

Statistical inference and evolutionary biology. Monday, January 27, 2014. Overcoming the fear of over-parameterization. For me, shunning rich and flexible models out of fear of over-parameterization is pure superstition - a bit like refraining from the pleasures of life out of fear of going to hell. There are at least three different reasons why I think that this fear of over-parameterization is irrational. It automatically adjusts to what is needed by the data. There are of course a few delicate issues ...

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bayesiancook.blogspot.com

The Bayesian kitchen

Statistical inference and evolutionary biology. Thursday, November 13, 2014. Bayesian diversification models (2). Concerning my last post, I realize that my notation for rejection sampling models is a bit elliptic. In particular, the rejection step corresponding to the fact that we condition on the observed data is never explicitly written. So, let me just reformulate the technical arguments and restate the main idea. Sample $ theta$ from prior. Simulate data given $ theta$. UNTIL simulated phylogeny mat...

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