Approximate Bayesian Computation for the inference of non-renewal point processes with application to neuroscience

Description

Here we consider a partially observed bivariate stochastic process and discuss it in the framework of stochastic modelling of single neuron dynamics. None of the two components is directly observed: the available observations correspond to hitting times of the first component to the second component. Our aim is to provide statistical inference of the underlying model parameters. This is particularly difficult since the considered process does not fit into the well-known class of hidden Markov models, requiring the investigation of new ad-hoc mathematical and statistical techniques to handle it. Here we present some preliminary results obtained performing {Approximate Bayesian Computation (ABC) (Beaumontetal,Sissonetal), using different distance criteria, e.g. Kolmogorov-Smirnov tests and the kth-Nearest Neighbors algorithm}.

Period	26 Mar 2018
Event title	Bayesian Computation
Event type	Conference
Location	SpainShow on map