Statistics seminar

​Sebastian Persson, Chalmers/GU: "Scalable Bayesian inference for dynamic state-space mixed-effects models"

​Abstract:

Parameter inference is an important step when constructing a dynamic model in many fields ranging from biology, medicine (PK/PD) to finance. In many scenarios, such as when modelling biological single-cell behaviour, we are interested in inference for an entire population by simultaneously fitting observations from multiple individuals. However, inference from multi-individual longitudal data is often non-trivial due to the presence of intrinsic and extrinsic sources of variability. To address this, we consider inference for the challenging case when the dynamics for a state-space mixed-effects model (SSMEM) are driven by stochastic processes such as a Markov-jump process or a stochastic differential equation. We present an efficient Gibbs-sampler for fully Bayesian inference for SSMEMs, which compared to previous samplers can be more than 30 times faster for many (>100) individuals. The individual parameters in the Gibbs-sampler, which have an intractable likelihood, are efficiently sampled via correlated-particle pseudo-marginal Metropolis-Hastings' steps. The population parameters of the random effects, which have a tractable likelihood, are updated using a HMC sampler to allow for a realistic parameterization of the individual parameters. 

The performance of our Gibbs sampler is investigated on challenging simulated datasets (e.g., a stochastic bi-stable model) and on a real-life dataset. Furthermore, we investigate the performance of different adaptive MCMC algorithms for the pseudo-marginal steps.
​Organiser: Umberto Picchini (picchini@chalmers.se). Please contact me if you wish to be informed of future seminars.

The seminar will be both online and in room MVL15. Members of the department will receive the link and password via mail. Others interested are welcome to get the link by contacting picchini@chalmers.se
Category Seminar
Location: MVL15 and online
Starts: 30 November, 2021, 14:00
Ends: 30 November, 2021, 15:00

Page manager Published: Fri 26 Nov 2021.