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Bounded Bayes: Markov Chain Monte Carlo (MCMC) for Posteriors of Bounded Parameters

This is largely a note to my past-self on how to easily use Markov Chain Monte Carlo (MCMC) methods for Bayesian inference when the parameter you are interested in has bounded support. The most basic MCMC methods involve using additive noise to get new draws, which can cause problems if that kicks you out of the parameter space. Suggestions abound to use the transformation trick on a bounded parameter \(\theta\), and then make draws of the transformed parameter.



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Bounded Bayes: Markov Chain Monte Carlo (MCMC) for Posteriors of Bounded Parameters

https://ddarmon.github.io/post/bounded-bayes

This is largely a note to my past-self on how to easily use Markov Chain Monte Carlo (MCMC) methods for Bayesian inference when the parameter you are interested in has bounded support. The most basic MCMC methods involve using additive noise to get new draws, which can cause problems if that kicks you out of the parameter space. Suggestions abound to use the transformation trick on a bounded parameter \(\theta\), and then make draws of the transformed parameter.



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https://ddarmon.github.io/post/bounded-bayes

Bounded Bayes: Markov Chain Monte Carlo (MCMC) for Posteriors of Bounded Parameters

This is largely a note to my past-self on how to easily use Markov Chain Monte Carlo (MCMC) methods for Bayesian inference when the parameter you are interested in has bounded support. The most basic MCMC methods involve using additive noise to get new draws, which can cause problems if that kicks you out of the parameter space. Suggestions abound to use the transformation trick on a bounded parameter \(\theta\), and then make draws of the transformed parameter.

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      Bounded Bayes: Markov Chain Monte Carlo (MCMC) for Posteriors of Bounded Parameters
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      This is largely a note to my past-self on how to easily use Markov Chain Monte Carlo (MCMC) methods for Bayesian inference when the parameter you are interested in has bounded support. The most basic MCMC methods involve using additive noise to get new draws, which can cause problems if that kicks you out of the parameter space. Suggestions abound to use the transformation trick on a bounded parameter \(\theta\), and then make draws of the transformed parameter.
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      Bounded Bayes: Markov Chain Monte Carlo (MCMC) for Posteriors of Bounded Parameters
    • twitter:description
      This is largely a note to my past-self on how to easily use Markov Chain Monte Carlo (MCMC) methods for Bayesian inference when the parameter you are interested in has bounded support. The most basic MCMC methods involve using additive noise to get new draws, which can cause problems if that kicks you out of the parameter space. Suggestions abound to use the transformation trick on a bounded parameter \(\theta\), and then make draws of the transformed parameter.

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      Bounded Bayes: Markov Chain Monte Carlo (MCMC) for Posteriors of Bounded Parameters
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      This is largely a note to my past-self on how to easily use Markov Chain Monte Carlo (MCMC) methods for Bayesian inference when the parameter you are interested in has bounded support. The most basic MCMC methods involve using additive noise to get new draws, which can cause problems if that kicks you out of the parameter space. Suggestions abound to use the transformation trick on a bounded parameter \(\theta\), and then make draws of the transformed parameter.
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