Yuting Chen (Ecole Centrale Paris): A comparison of Sequential Monte Carlo techniques for parameter estimation in a plant growth model

Plant models have specific characteristics that make their parameter estimation difficult. Models are usually strongly nonlinear, with a strong mechanistic basis, they count many unknown parameters and often only scarce experimental data are available for model inference. Because of their mechanistic basis, there is generally some available knowledge on the biological processes at stake (owing to previous literature studies on the same processes) and it is possible to deduce some a priori distribution on the model parameters. This fact, together with the limited number of data, makes the Bayesian choice generally interesting for parameter estimation in plant growth models. In this study, we compare two approaches for Bayesian inference that are adapted to the characteristics of plant models. We first consider a MCMC algorithm implemented with Adaptive Metropolis and second, the Convolution Particle Filter (Rossi and Vila, 2006; Campillo and Rossi, 2009) which is inspired by the post-regularized particle filter (Musso and Oudjane, 1998). The performance of these two approaches are tested for the LNAS dynamic model of plant growth (Cournede et al., 2013), formalized as a general state space hidden Markov model (Cappé et al., 2005). We show that both methods perform well with virtual data, however in realistic scenarios with sparse observations, the filtering method appears more consistent. New implementations of multiple interacting MCMC techniques are thus tested to improve the performance of the MCMC approach (Campillo et al., 2009).

Joint work with Samis Trevezas, Sonia Malefaki, Paul-Henry Cournède.