'Prior Exposure' Bayesian data analysis (BDA) workshop 4: Nonlinear and latent variable models
Date:
23/09/2015
Organised by:
Nottingham Trent University
Presenter:
Dr Mark Andrews
Level:
Advanced (specialised prior knowledge)
Contact:
Professor Thom Baguley
thomas.baguley@ntu.ac.uk
Map:
View in Google Maps (NG1 5LT)
Venue:
Chaucer Building, Nottingham Trent University, Goldsmiths Street, Nottingham
Description:
This final workshop focuses on Bayesian latent variable modeling, particularly using mixture models. Mixture models, also known as latent class models, model probability distributions as finite or infinite sums of simple component distributions. As such, mixture models provide a general means for modeling unique and arbitrarily complex probability distributions. They are also routinely used in practice, particularly in psychometrics. Bayesian approaches to mixture modeling rely heavily on Dirichlet prior distributions over finite numbers of mixture component, and Dirichlet process priors over infinitely many components. These Dirichlet process mixture models provide an elegant solution to the otherwise formidable challenge of inferring the correct number of components in mixture models. This workshop also focuses on Bayesian approaches to nonlinear regression modeling using Gaussian process models. Gaussian process regression represents a unifying approach to nonlinear regres- sion, with many particular approaches to nonlinear regression - radial basis function, multilayer perceptrons, splines - being special cases of this general form.
Learning outcomes
On completion of this workshop, we expect attendees to be able to confidently perform advanced regression and latent variable modeling.
Prerequisites
The skills and knowledge provided in Workshops 1, 2 & 3 fulfill the prerequisites for this work- shop. Knowledge of the fundamentals of Bayesian inference, particularly in complex and multilevel models, and competence with R and BUGS/JAGS is assumed.
Representative readings
- Rasmussen, C. E., Williams, C. K. I. (2006). Gaussian processes for machine learning. The MIT Press.
- Neal, R. M. (2000). Markov chain sampling methods for dirichlet process mixture models. Journal of Computational and Graphical Statistics, 9, 249-265.
Cost:
£10 (research students)
£20 (others)
Website and registration:
http://www.priorexposure.org.uk
Region:
East Midlands
Keywords:
Bayesian methods, Markov Chain Monte Carlo (MCMC), Regression Methods, Multilevel Modelling
Related publications and presentations:
Bayesian methods
Markov Chain Monte Carlo (MCMC)
Regression Methods
Multilevel Modelling