Dennis Prangle |

Tue 23 May 2017, 11:00 - 12:00 |

IF 4.31/4.33 |

If you have a question about this talk, please contact: Gareth Beedham (gbeedham)

**"Rare event methods for approximate Bayesian computation"**

Many stochastic mathematical models are hard to fit due to only partial observations being available. An example is fitting infectious disease epidemic models to learn rates of infection and recovery. This is straightforward if the times of all relevant events (e.g. population members being infected and recovering) are known, but in reality only some of these are available.

One approach to this problem is approximate Bayesian computation (ABC).

This is useful in situations where simulation of data from the model can be performed quickly. The idea is to find parameter values such that corresponding model simulations match the observations well. A problem is that this scales poorly to high dimensional data as good matches become extremely rare.

This talk will review the basics of ABC and present recent work on improving its performance using rare event methods. The idea is to estimate the rare probability of a simulation matching the data well. To do this we introduce latent variables, which represent all the random unobservable quantities which drive the simulation. Our method uses a systematic search of the space of latent variables to estimate the rare probability of interest, and uses this in model fitting.

I'll present theoretical and empirical support for our method, and discuss other possibilities of using latent variables to scale up ABC methods.