06 May 2023
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.
401 May 2023
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.
529 April 2023
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.
613 April 2023
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.
712 April 2023
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.
811 April 2023
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.
9The Beta-Binomial Bayesian Model
08 April 2023
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.
10Conjugate Priors - Binomial Beta Pair
27 March 2023
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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