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Nonlinear and Latent Variable Models - Workshop 4

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Course Information

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This final workshop presents a brief overview and recap of methods for advanced Bayesian data analysis before introducing Bayesian latent variable modeling, particularly those using mixture models.

Booking Option Fee
Full Workshop Fee £20.00
Full Workshop Fee - Postgraduate student rate £10.00


Course Code

ESRC Workshop 4

Course Date

28th September 2017

Places Available

Course Fee

Course Description

Time: 9 am - 5 pm

Location: Room 5108, Chaucer building, Nottingham Trent University, NG1 5LT


Mixture models, also known as latent class models, enable researchers to 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 regression, with many particular approaches to nonlinear regression - radial basis function, multilayer perceptrons, splines - being special cases of this general form.


The skills and knowledge provided in workshops one, two and three fulfil the prerequisites for this workshop. Knowledge of the fundamentals of Bayesian inference, particularly in complex and multilevel models, and competence with R and BUGS/JAGS is assumed.

Learning outcomes

On completion of this workshop, we expect attendees to be able to confidently perform advanced regression and latent variable modeling.

Indicative reading

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.


Bursaries are available to apply for, to assist with costs associated with attending this workshop. Please see the More Info tab for further information. Please note that only PhD students who attend UK HE institutions are eligible to apply for this bursary.