Tag: Bayesian Updating

Bayesian Inference VII – Bayesian Updating Continuous Priors and Data

We finally got to the part of Bayesian Updating when both data and prior are continuous. It is, like always, not very different from Bayesian Updating with discrete data and priors except of that we use the PDFs and not the PMFs. Continuous Data, Continuous Prior When we use probability density functions we have to … Continue reading Bayesian Inference VII – Bayesian Updating Continuous Priors and Data

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Bayesian Inference V – Bayesian Updating Continuous Priors

We already know how to do Bayesian Updating with discrete priors. Today we will learn how to do Bayesian Updating with continuous priors. Continous Priors To do Bayesian Updating with continuous priors but with discrete data - we will look at the case that both is discrete next time - we just change sums to … Continue reading Bayesian Inference V – Bayesian Updating Continuous Priors

Experiment – Monty Hall Problem

After I'd covered the Monty Hall Problem in the last blog-post, I wrote an experiment in python, which shows that when the number of doors increases but the rules of the game stay the say, our probability of winning given that we switched converges to 1. The probability of winning given we stayed converges to … Continue reading Experiment – Monty Hall Problem

Bayesian Inference II – Bayesian Updating Discrete Priors

We get closer and closer to the exciting, interesting parts of data science. Bayesian Inference or more precisely Bayesian updating is one part of that. It is used in some machine learning algorithms and allows us to update probabilities when we get new data. Bayesian Updating Discrete Priors We will today just look at discrete … Continue reading Bayesian Inference II – Bayesian Updating Discrete Priors