Tag: bayesian-statistics

The connection between entropy and probability distributions is really interesting. In this post, I will explore the connection between entropy and probability distributions, and how we can use this connection to derive the most likely probability distribution given some constraints.

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Modern Bayesian statistics relies on models for which the posterior is not easy to compute and corresponding algorithms for approximating them. Variational inference is one of the most popular methods for approximating the posterior. In this post, we will introduce the basic idea of variational inference and its application to a simple example.

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Modern Bayesian statistics relies on models for which the posterior is not easy to compute and corresponding algorithms for approximating them. Variational inference is one of the most popular methods for approximating the posterior. In this post, we will introduce the basic idea of variational inference and its application to a simple example.

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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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