10 papers
Recursive Maximum Likelihood Estimation for Interacting Particle Systems using Virtual Particles
Louis Sharrock, Nikolas Kantas, Grigorios A. Pavliotis
We study recursive maximum likelihood estimation for stochastic interacting particle systems based on continuous observation of a single particle. In this regime, consistent estima…
Mixing Time Bounds for the Gibbs Sampler under Isoperimetry
Alexander Goyal, George Deligiannidis, Nikolas Kantas
We establish bounds on the conductance for the systematic-scan and random-scan Gibbs samplers when the target distribution satisfies a Poincaré or log-Sobolev inequality and posse…
Particle Filtering for a Class of State-Space Models with Low and Degenerate Observational Noise
Abylay Zhumekenov, Alexandros Beskos, Dan Crisan +2
We consider the discrete-time filtering problem in scenarios where the observation noise is low or degenerate. We focus on the case where the observation equation is a linear funct…
Smoothness and other hyperparameter estimation for inverse problems related to data assimilation
Baptiste Simandoux, Nikolas Kantas, Dan Crisan
We consider Bayesian inverse problems arising in data assimilation for dynamical systems governed by partial and stochastic partial differential equations. The space-time dependent…
Efficient Online Learning in Interacting Particle Systems
Louis Sharrock, Nikolas Kantas, Grigorios A. Pavliotis
We introduce a new method for online parameter estimation in stochastic interacting particle systems, based on continuous observation of a small number of particles from the system…
Drift Estimation for Stochastic Differential Equations with Denoising Diffusion Models
Marcos Tapia Costa, Nikolas Kantas, George Deligiannidis
We study the estimation of time-homogeneous drift functions in multivariate stochastic differential equations with known diffusion coefficient, from multiple trajectories observed…