Training and Events
'Prior Exposure' Bayesian data analysis (BDA) workshop 4: Nonlinear and latent variable models
|Nottingham Trent University|
Dr Mark Andrews
Chaucer Building, Nottingham Trent University, Goldsmiths Street, Nottingham
View in Google Maps (NG1 5LT)
Professor Thom Baguley
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.
On completion of this workshop, we expect attendees to be able to confidently perform advanced regression and latent variable modeling.
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.
Advanced (specialised prior knowledge)
£10 (research students)
Website and registration
Bayesian methods, Markov Chain Monte Carlo (MCMC), Regression Methods, Multilevel Modelling
Related publications and presentations