5 papers
Similarity-Driven Proposals for MCMC Algorithms on Discrete Spaces
Luca Aiello, Raffaele Argiento, Alexandros Beskos +1
Recent research has led to the development of MCMC algorithms with likelihood-informed proposals when targeting posterior distributions supported on discrete state spaces. Our work…
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…
Sequential Markov Chain Monte Carlo for Filtering of State-Space Models with Low or Degenerate Observation Noise
Abylay Zhumekenov, Alexandros Beskos, Dan Crisan +2
We consider the discrete-time filtering problem in scenarios where the observation noise is degenerate or low. More precisely, one is given access to a discrete time observation se…
A Closed-Form Transition Density Expansion for Elliptic and Hypo-Elliptic SDEs
Yuga Iguchi, Alexandros Beskos
We introduce a closed-form expansion for the transition density of elliptic and hypo-elliptic multivariate Stochastic Differential Equations (SDEs), over a period , in…
Antithetic Multilevel Methods for Elliptic and Hypo-Elliptic Diffusions with Applications
Yuga Iguchi, Ajay Jasra, Mohamed Maama +1
We present a new antithetic multilevel Monte Carlo (MLMC) method for the estimation of expectations with respect to laws of diffusion processes that can be elliptic or hypo-ellipti…