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Parfait Munezero: Online Predictive Finite Mixture of Poisson Models

Tid: On 2019-03-13 kl 13.00 - 14.00

Föreläsare: Parfait Munezero, Department of Statistics

Plats: Room B705, Department of Statistics, Stockholm University

Abstract: We introduce a finite mixture of Poisson regression models with component distributions and mixing weights that depend on a set of covariates whose effect changes over time. The effect parameters are modeled in a semi-parametric way using piecewise constant functions. Inference is done in a Bayesian framework and the marginal particle filter algorithm is used to sample from the online posterior distribution. Generally, the performance of particle filter algorithms depends largely on the proposal distribution; we therefore design a proposal distribution tailored to the model using linear Bayes theory. We apply the model to a real dataset consisting of a history of faults reported on a series of a software upgrade releases. Preliminary results show that allowing the parameters to evolve over time greatly improves predictive performance. Further, we assess the performance of the model using different simulation scenarios.

Tillhör: Institutionen för matematik
Senast ändrad: 2019-03-08