Smoothing distributions for conditional Fleming-Viot and Dawson-Watanabe diffusions

TL;DR

This paper derives explicit recursive formulas for the smoothing distributions of measure-valued Fleming-Viot and Dawson-Watanabe diffusions, enabling better inference of unobserved population states over time.

Contribution

It provides the first explicit recursive characterization of smoothing distributions for these complex measure-valued diffusions, including their time-dependent mixture weights.

Findings

Smoothing distributions are finite mixtures of Dirichlet and gamma laws.

Time-dependent mixture weights are explicitly characterized.

Predictive distributions are mixtures of generalized Polya urns.

Abstract

We study the distribution of the unobserved states of two measure-valued diffusions of Fleming-Viot and Dawson-Watanabe type, conditional on observations from the underlying populations collected at past, present and future times. If seen as nonparametric hidden Markov models, this amounts to finding the smoothing distributions of these processes, which we show can be explicitly described in recursive form as finite mixtures of laws of Dirichlet and gamma random measures respectively. We characterize the time-dependent weights of these mixtures, accounting for potentially different time intervals between data collection times, and fully describe the implications of assuming a discrete or a nonatomic distribution for the underlying process that drives mutations. In particular, we show that with a nonatomic mutation offspring distribution, the inference automatically upweights mixture components that carry, as atoms, observed types shared at different collection times. The predictive distributions for further samples from the population conditional on the data are also identified and shown to be mixtures of generalized Polya urns, conditionally on a latent variable in the Dawson-Watanabe case.