Laplace approximation in multi-type data modelling with multivariate Gaussian process regression and applications

Activity: Talk or presentation typesOral presentation


With the fast pace development of data management softwares (e.g. GIS) a great variety of practical applications can provide rich databases which are fraught with different data types (e.g. discrete, continuous). Statistical data analysis of multiple data types often require multivariate probabilistic models. However, in this case, the choice of the multivariate probabilistic model may be difficult due to this mixture of types. An alternative way to circumvent this issue is to formulate the model from the hierarchical viewpoint. In this way, well-known probabilistic models can still be used and dependency between data types can be introduced in the second layer of the hierarchy. In this presentation we will introduce a hierarchical model where the vector of predictor functions (in the sense of generalised linear models) is assumed to follow a multivariate Gaussian process. Statistical inference over the vector of predictor functions is approached by the means of the Bayes' rule with the Laplace approximation. These ideas have been motivated by applications in quantitative ecology and species distribution modelling. Some examples are presented.
Period3 Jun 2019
Held atNTNU (Norges teknisk-naturvitenskapelige universitet), Norway
Degree of RecognitionLocal