181 citations · 755 across the 107 of their papers we have counts for
10 papers · 2 filters
Parameter Estimation in Hidden Markov Models with Intractable Likelihoods Using Sequential Monte Carlo
Sinan Yildirim, Sumeetpal Singh, Thomas Dean +1
We propose sequential Monte Carlo based algorithms for maximum likelihood estimation of the static parameters in hidden Markov models with an intractable likelihood using ideas fro…
Sequential Monte Carlo Methods for High-Dimensional Inverse Problems: A case study for the Navier-Stokes equations
Nikolas Kantas, Alexandros Beskos, Ajay Jasra
We consider the inverse problem of estimating the initial condition of a partial differential equation, which is only observed through noisy measurements at discrete time intervals…
Computational Methods for a Class of Network Models
Junshan Wang, Ajay Jasra, Maria De Iorio
In the following article we provide an exposition of exact computational methods to perform parameter inference from partially observed network models. In particular, we consider t…
On the Convergence of Adaptive Sequential Monte Carlo Methods
Alexandros Beskos, Ajay Jasra, Nikolas Kantas +1
In several implementations of Sequential Monte Carlo (SMC) methods it is natural, and important in terms of algorithmic efficiency, to exploit the information of the history of the…
Variational inference for sparse spectrum Gaussian process regression
Linda S. L. Tan, Victor M. H. Ong, David J. Nott +1
We develop a fast variational approximation scheme for Gaussian process (GP) regression, where the spectrum of the covariance function is subjected to a sparse approximation. Our a…
An Adaptive Sequential Monte Carlo Algorithm for Computing Permanents
Ajay Jasra, Junshan Wang
We consider the computation of the permanent of a binary n by n matrix. It is well- known that the exact computation is a #P complete problem. A variety of Markov chain Monte Carlo…
The Alive Particle Filter
Ajay Jasra, Anthony Lee, Christopher Yau +1
In the following article we develop a particle filter for approximating Feynman-Kac models with indicator potentials. Examples of such models include approximate Bayesian computati…
Approximate Inference for Observation Driven Time Series Models with Intractable Likelihoods
Ajay Jasra, Nikolas Kantas, Elena Ehrlich
In the following article we consider approximate Bayesian parameter inference for observation driven time series models. Such statistical models appear in a wide variety of applica…
The TimeMachine for Inference on Stochastic Trees
Gianluca Campanella, Maria De Iorio, Ajay Jasra +1
The simulation of genealogical trees backwards in time, from observations up to the most recent common ancestor (MRCA), is hindered by the fact that, while approaching the root of…
A sequential algorithm for fast fitting of Dirichlet process mixture models
David Nott, Xiaole Zhang, Chris Yau +1
In this article we propose an improvement on the sequential updating and greedy search (SUGS) algorithm Wang and Dunson for fast fitting of Dirichlet process mixture models. The SU…