INTRODUCTION TO BAYESIAN INFERENCE USING RSTAN
Date:
10/04/2017 - 11/04/2017
Organised by:
Jumping Rivers ltd
Presenter:
Dr Sarah Heap & Dr Jamie Owen
Level:
Intermediate (some prior knowledge)
Contact:
Esther Gillespie, esther@jumpingrivers.com, 07740285328
Map:
View in Google Maps (WC1E 7HU)
Venue:
University of London, Senate House, University of London, Malet St, London WC1E 7HU
Description:
LONDON at London University Senate House
This is an intensive, two day course introducing the use of RStan for Bayesian computation. The course will be a mixture of lectures and computer practicals. The main focus will be on the specification of models using the Stan language and on the practicalities of generating samples from the posterior distribution and diagnosing convergence.
PREREQUISITES
Partipants should be familiar with basic Probability and Statistics including common distributions and regression. Basic R 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.
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 RStan: A guide to the use of the R interface to Stan.
- Examples: Including linear regression, Poisson regression and hierarchical models.
COURSE STRUCTURE
This course will consist of short lectures, followed by short practical sessions.
PRESENTER
Dr Sarah Heap, Statistics Lecturer in the School of Mathematics &Statistics. Expert in Big Data, Bayesian Statistics and Time Series analysis.
Cost:
£850 + Vat (25% discount to academics & charity)
Website and registration:
Region:
Greater London
Keywords:
Secondary Analysis, Digital Social Research, Mixed Methods, Data Collection (other), Qualitative Data Handling and Data Analysis, Quantitative Data Handling and Data Analysis, Statistical Theory and Methods of Inference, Mixed Methods Data Handling and Data Analysis, ICT and Software, R, Data Visualisation, Creating graphs and charts, Interactive data visualisation, Workshops, Training research methods teachers
Related publications and presentations:
Secondary Analysis
Digital Social Research
Mixed Methods
Data Collection (other)
Qualitative Data Handling and Data Analysis
Quantitative Data Handling and Data Analysis
Statistical Theory and Methods of Inference
Mixed Methods Data Handling and Data Analysis
ICT and Software
R
Data Visualisation
Creating graphs and charts
Interactive data visualisation
Workshops
Training research methods teachers