Neural Likelihood Approximation for Integer Valued Time Series Data

arXiv:2310.12544v2 Announce Type: replace-cross
Abstract: Stochastic processes defined on integer valued state spaces are popular within the physical and biological sciences. These models are necessary for capturing the dynamics of small systems where the individual nature of the populations cannot be ignored and stochastic effects are important. The inference of the parameters of such models, from time series data, is challenging due to intractability of the likelihood. To work at all, current simulation based inference methods require the generation of …

abstract approximation arxiv biological sciences cs.lg data dynamics effects inference likelihood nature popular processes series small spaces state stat.ml stochastic systems time series type