Projects per year
Abstract / Description of output
We consider continuous time Markovian processes where populations of individual agents interact stochastically according to kinetic rules. Despite the increasing prominence of such models in fields ranging from biology to smart cities, Bayesian inference for such systems remains challenging, as these are continuous time, discrete state systems with potentially infinite state-space. Here we propose a novel efficient algorithm for joint state / parameter posterior sampling in population Markov Jump processes. We introduce a class of pseudo-marginal sampling algorithms based on a random truncation method which enables a principled treatment of infinite state spaces. Extensive evaluation on a number of benchmark models shows that this approach achieves considerable savings compared to state of the art methods, retaining accuracy and fast convergence. We also present results on a synthetic biology data set showing the potential for practical usefulness of our work.
Keywords / Materials (for Non-textual outputs)
- Markov Jump Processes
- Markov Chain Monte Carlo
- Pseudo-marginal methods
- Parameter estimation
- Stochastic processes
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- 2 Finished
QUANTICOL - A Quantitative Approach to Management and Design of Collective and Adaptive Behaviours (RTD)
1/04/13 → 31/03/17
MLCS - Machine learning for computational science statistical and formal modeling of biological systems
1/10/12 → 30/09/17