Tag: beta-distribution

The Metropolis-Hastings algorithm is a Markov chain Monte Carlo (MCMC) algorithm that generates a sequence of random variables from a probability distribution from which direct sampling is difficult.

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When we use Bayesian inference, we need to compute the posterior distribution. In this post, we will look at some methods for approximating the posterior distribution.

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When we build a model, we need to choose a prior distribution. If we choose a prior distribution from the same family as the posterior distribution, we can use the posterior distribution as the new prior distribution. This is called a conjugate prior. In this post, we will look at some of the most common conjugate priors.

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With more data generating day by day, I believe Bayesian statistics is the way to go. That's why I'm writing this series of posts on Bayesian statistics. In this post, I'll introduce the Beta-Binomial Bayesian model again. I'll also show how two communities (Python and R) have implemented this model.

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Bayesian inference is almost 'everywhere' in data science; with the advance of computational power, it is now possible to apply Bayesian inference to high-dimensional data. In this post, we will discuss the conjugate priors for the binomial distribution.

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