Bayesian inference for a partially observed birth-death process using data on proportions

TL;DR

This paper develops Bayesian inference methods for partially observed birth-death processes, especially when data is limited to noisy proportion measurements, addressing challenges in biological process modeling.

Contribution

It introduces Bayesian algorithms tailored for data-poor scenarios with limited proportion observations in birth-death processes, enhancing inference capabilities.

Findings

Bayesian methods effectively estimate parameters with limited data.

Algorithms perform well with noisy proportion observations.

Improved inference in biologically relevant, data-scarce settings.

Abstract

Stochastic kinetic models are often used to describe complex biological processes. Typically these models are analytically intractable and have unknown parameters which need to be estimated from observed data. Ideally we would have measurements on all interacting chemical species in the process, observed continuously in time. However, in practice, measurements are taken only at a relatively few time-points. In some situations, only very limited observation of the process is available, such as when experimenters can only observe noisy observations on the proportion of cells that are alive. This makes the inference task even more problematic. We consider a range of data-poor scenarios and investigate the performance of various computationally intensive Bayesian algorithms in determining the posterior distribution using data on proportions from a simple birth-death process.