20 Result(s)
within
Ajay Jasra
Probability and Statistics in Computer Science
-
Article
Bayesian parameter inference for partially observed stochastic volterra equations
In this article we consider Bayesian parameter inference for a type of partially observed stochastic Volterra equation (SVE). SVEs are found in many areas such as physics and mathematical finance. In the latte...
-
Article
Bayesian parameter inference for partially observed stochastic differential equations driven by fractional Brownian motion
In this paper we consider Bayesian parameter inference for partially observed fractional Brownian motion models. The approach we follow is to time-discretize the hidden process and then to design Markov chain ...
-
Article
Open AccessA 4D-Var method with flow-dependent background covariances for the shallow-water equations
The 4D-Var method for filtering partially observed nonlinear chaotic dynamical systems consists of finding the maximum a-posteriori (MAP) estimator of the initial condition of the system given observations ove...
-
Article
Multilevel estimation of normalization constants using ensemble Kalman–Bucy filters
In this article we consider the application of multilevel Monte Carlo, for the estimation of normalizing constants. In particular we will make use of the filtering algorithm, the ensemble Kalman–Bucy filter (E...
-
Article
Unbiased approximation of posteriors via coupled particle Markov chain Monte Carlo
Markov chain Monte Carlo (MCMC) is a powerful methodology for the approximation of posterior distributions. However, the iterative nature of MCMC does not naturally facilitate its use with modern highly parall...
-
Article
Open AccessUncertainty modelling and computational aspects of data association
A novel solution to the smoothing problem for multi-object dynamical systems is proposed and evaluated. The systems of interest contain an unknown and varying number of dynamical objects that are partially obs...
-
Article
Open AccessUnbiased estimation of the gradient of the log-likelihood in inverse problems
We consider the problem of estimating a parameter \(\theta \in \Theta \subseteq {\mathbb {R}}^{d_{\theta }}\) ...
-
Article
Multilevel particle filters for the non-linear filtering problem in continuous time
In the following article we consider the numerical approximation of the non-linear filter in continuous-time, where the observations and signal follow diffusion processes. Given access to high-frequency, but d...
-
Article
Open AccessCorrection to: Multilevel particle filters for Lévy-driven stochastic differential equations
The article Multilevel particle filters for Lévy-driven stochastic differential equations, written by Ajay Jasra, Kody J. H. Law, Prince Peprah Osei, was originally published electronically on the publisher’s ...
-
Article
Open AccessMultilevel particle filters for Lévy-driven stochastic differential equations
We develop algorithms for computing expectations with respect to the laws of models associated to stochastic differential equations driven by pure Lévy processes. We consider filtering such processes as well a...
-
Article
On coupling particle filter trajectories
Particle filters are a powerful and flexible tool for performing inference on state-space models. They involve a collection of samples evolving over time through a combination of sampling and re-sampling steps...
-
Article
Multilevel particle filters: normalizing constant estimation
In this article, we introduce two new estimates of the normalizing constant (or marginal likelihood) for partially observed diffusion (POD) processes, with discrete observations. One estimate is biased but non...
-
Article
Variational inference for sparse spectrum Gaussian process regression
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 approach enables uncertainty i...
-
Article
Monte Carlo algorithms for computing \(\alpha \) -permanents
We consider the computation of the \(\alpha \) α ...
-
Article
Sequential Monte Carlo methods for Bayesian elliptic inverse problems
In this article, we consider a Bayesian inverse problem associated to elliptic partial differential equations in two and three dimensions. This class of inverse problems is important in applications such as hy...
-
Article
Bayesian parameter inference for partially observed stopped processes
We consider Bayesian parameter inference associated to partially-observed stochastic processes that start from a set B 0 and are stopped or killed at the first hitting time of a known set A. Such ...
-
Article
Filtering via approximate Bayesian computation
Approximate Bayesian computation (ABC) has become a popular technique to facilitate Bayesian inference from complex models. In this article we present an ABC approximation designed to perform biased filtering ...
-
Article
An adaptive sequential Monte Carlo method for approximate Bayesian computation
Approximate Bayesian computation (ABC) is a popular approach to address inference problems where the likelihood function is intractable, or expensive to calculate. To improve over Markov chain Monte Carlo (MCM...
-
Article
Stochastic boosting algorithms
In this article we develop a class of stochastic boosting (SB) algorithms, which build upon the work of Holmes and Pintore (Bayesian Stat. 8, Oxford University Press, Oxford, 2007). They introduce boosting algori...
-
Article
On population-based simulation for static inference
In this paper we present a review of population-based simulation for static inference problems. Such methods can be described as generating a collection of random variables {X ...
