Bayesian Data Analysis

Overview

Bayesian inference is a powerful and increasingly popular statistical approach. It can deal with complicated problems where classical frequentist analysis would be difficult to apply. Indeed, Bayesian data analysis and frequentist methods provide different ways to draw conclusions from data and address random variation. While classical frequentist approaches calculate the probability of observing data given a specific value of a parameter, Bayesian statistics provide conditional probabilities for the different values of a parameter given the data. In other words, Bayesian approach allows scientists to combine new data with their existing knowledge or expertise.
Firstly facing some computational challenges for large problems solving, recent advances in computational techniques and especially the discovery of Markov Chain Monte Carlo (MCMC) simulation methods have led to an explosion of interest in Bayesian statistics and modelling in many areas including computational biology and ecology.

The course will be centered on "bayesian data analysis" applied to biological problems. Topics addressed during this course include single-and multi-parameter bayesian models, hierarchical models and bayesian computation technics (MCMC).

Audience

This course is intended for life scientists who already have some good knowledge of statistics and the programming language "R".

Learning objectives

At the end of this course, participants are expected to be able to:

• understand the difference between a frequentist and a bayesian approach
• compute the posterior density in a simple case
• understand the basics of hierarchical modeling
• get a basic idea of the different bayesian computational methods
• perform a bayesian analysis on real data

Prerequisites

Knowledge / competencies:

The course is intended to people with a good knowledge of statistics and R. In particular people who have already followed the course  "Advanced statistics" or a similar advanced course. Participants must be comfortable with topics such as likelihood, inference, modeling and must have a good prior knowledge of "R" language and environment.

Technical:

Participants must bring a laptop and the "R" software installed. More information about the packages needed will be provided before the course.

Application

The registration fees for academics are 200 CHF. This includes course content material, coffee breaks, and a social dinner. Participants from non-academic institutions should contact us before application.

Deadline for registration and free-of-charge cancellation is set to August 26, 2016. Cancellation after this date will not be reimbursed. Please note that participation to SIB courses is subject to this and other general conditions, available here.

You will be informed by email of your registration confirmation. Upon reception of the confirmation email, participants will be asked to confirm attendance by paying the fees within 5 days.

Location

University of Lausanne, Génopode Building, room 2020 (UNIL sorge M1 line stop)

Additional information

Coordination: Diana Marek

You are welcome to register to the SIB courses mailing-list to be informed of all future courses and workshops, as well as all important deadlines using the form here.

For more information, please contact training@sib.swiss.