Neural Likelihood Approximation for Integer Valued Time Series Data

Stochastic processes defined on integer valued state spaces are popular within the physical A combination of factors such as non-linear dynamics, and biological sciences. These models are partial observation and a complicated latent structure necessary for capturing the dynamics of small makes the inference of parameter posteriors a systems where the individual nature of the challenging problem. The likelihood is generally intractable populations cannot be ignored and stochastic and although methods that can sample from effects are important. The inference of the the exact posteriors exist--many based on simulation parameters of such models, from time series of the model itself--these can become computationally data, is difficult due to intractability of the prohibitive in many situations (Doucet et al., 2015; likelihood; current methods, based on simulations Sherlock et al., 2015). Observations of a system with of the underlying model, can be so computationally low noise are particularly challenging for simulation expensive as to be prohibitive.