Probability concepts explained: Marginalisation | by Jonny Brooks-Bartlett | TDS Archive | Medium

An archive of data science, data analytics, data engineering, machine learning, and artificial intelligence writing from the former Towards Data Science Medium publication. Follow publication Top highlight 2.2K 11 Listen Share In this post I’ll explain the concept of marginalisation and go through an example in the context of solving a fairly simple maximum likelihood problem. This post requires some knowledge of fundamental probability concepts which you can find explained in my introductory blog post in this series. Marginalisation is a method that requires summing over the possible values of one variable to determine the marginal contribution of another. That definition may sound a little abstract so let’s try to illustrate this with an example Suppose we’re interested in how the weather affects someone’s happiness in the United Kingdom (UK). We can write this mathematically as P(happiness|weather) i.e. what’s the probability of someone’s happiness level given the type of weather. S

An archive of data science, data analytics, data engineering, machine learning, and artificial intelligence writing from the former Towards Data Science Medium publication. Follow publication Top highlight 2.2K 11 Listen Share In this post I’ll explain the concept of marginalisation and go through an example in the context of solving a fairly simple maximum likelihood problem. This post requires some knowledge of fundamental probability concepts which you can find explained in my introductory blog post in this series. Marginalisation is a method that requires summing over the possible values o