Online | July 22nd, 2021,

Price:
Venue Details:
Start Date:
Time:
Duration:

Select if you qualify for a discount

Academic / Student Discount (-25%)

Participant(s)

Add Booking Add All Courses in this Series Ask For More Details About This Course

Course Details

Course Outline
Learning Outcomes
Materials
Prior Knowledge

Course Outline

Introduction to Bayesian inference: A brief overview of the main ideas behind Bayesian inference.
Markov chain Monte Carlo methods: A brief overview of Markov chain Monte Carlo methods for Bayesian computation and Hamiltonian Monte Carlo.
The Stan language: An outline of the main components of a Stan program.
Using PyStan: A guide to the use of the Python interface to Stan.
Examples: Including linear regression, Poisson regression and hierarchical models.

Learning Outcomes

By the end of the two days participants will…

have a good understanding of Bayesian inference/MCMC methods
understand how these methods can be applied to their own data
understand how to apply these methods to their own data using Python’s interface to Stan, PyStan.

Materials

Prior Knowledge

Participants should be familiar with basic Probability and Statistics including common distributions and regression. Basic Python programming is also required, i.e. writing loops and functions. We do not expect you to have experience with Bayesian Inference or Stan, but some knowledge of the former will be helpful.

Training Course Details

Introduction to Bayesian Inference using PyStan

Course Level: Intermediate