In this episode we talk about all things Bayesian. What is Bayesian inference and why is it the cornerstone of Data Science?Bayesian statistics embodies the Data Scientist and their role in the data modelling process. A Data Scientist starts with an idea of how to capture a particular phenomena in a mathematical model - maybe derived from talking to experts in the company. This represents the prior belief about the model. Then the model consumes data around the problem - historical data, real-time data, it doesn't matter. This data is used to update the model and the result is called the posterior.Why is this Data Science? Because models that react to data and refine their representation of the world in response to the data they see are what the Data Scientist is all about.We talk with Dr Joseph Walmswell, Principal Data Scientist at life sciences company Abcam, about his experience with Bayesian modelling. Further ReadingPublication list for Dr. Joseph Walmswell (https://bit.ly/3s8xluH via researchgate.net)Blog on Bayesian Inference for parameter estimation (https://bit.ly/2OX46fV via towardsdatascience.com)Book Chapter on Bayesian Inference (https://bit.ly/2Pi9Ct9 via cmu.edu)Article on The Monty Hall problem (https://bit.ly/3f1pefr via Wikipedia)Podcast on  "The truth about obesity and Covid-19", More or Less: Behind the Stats podcast (https://bbc.in/3lBqCGS via bbc.co.uk)Gov.uk guidance:Article on "Understanding lateral flow antigen testing for people without symptoms" (https://bit.ly/313JDs9)Article on "Households and bubbles of pupils, students and staff of schools, nurseries and colleges: get rapid lateral flow tests" (https://bit.ly/3c5ZXih)Some links above may require payment or login. We are not endorsing them or receiving any payment for mentioning them. They are provided as is. Often free versions of papers are available and we would encourage you to investigate.Recording date: 16 March 2021Interview date: 26 February 2021