48 citations · 190 across the 24 of their papers we have counts for
10 papers · 1 filter
Robust Bayesian inference in complex models with possibility theory
Jeremie Houssineau, David J. Nott
We propose a general solution to the problem of robust Bayesian inference in complex settings where outliers may be present. In practice, the automation of robust Bayesian analyses…
On Unbiased Score Estimation for Partially Observed Diffusions
Jeremy Heng, Jeremie Houssineau, Ajay Jasra
We consider the problem of statistical inference for a class of partially-observed diffusion processes, with discretely-observed data and finite-dimensional parameters. We construc…
On Large Lag Smoothing for Hidden Markov Models
Jeremie Houssineau, Ajay Jasra, Sumeetpal S. Singh
In this article we consider the smoothing problem for hidden Markov models (HMM). Given a hidden Markov chain and observations , our objectiv…
A linear algorithm for multi-target tracking in the context of possibility theory
Jeremie Houssineau
We present a modelling framework for multi-target tracking based on possibility theory and illustrate its ability to account for the general lack of knowledge that the target-track…
Parameter estimation with a class of outer probability measures
Jeremie Houssineau
We explore the interplay between random and deterministic phenomena using a representation of uncertainty based on the measure-theoretic concept of outer measure. The meaning of th…
Multilevel Monte Carlo for Smoothing via Transport Methods
Jeremie Houssineau, Ajay Jasra, Sumeetpal S. Singh
In this article we consider recursive approximations of the smoothing distribution associated to partially observed stochastic differential equations (SDEs), which are observed dis…
Sequential Monte Carlo algorithms for a class of outer measures
Jeremie Houssineau, Branko Ristic
Closed-form stochastic filtering equations can be derived in a general setting where probability distributions are replaced by some specific outer measures. In this article, we stu…
A second-order PHD filter with mean and variance in target number
Isabel Schlangen, Emmanuel D. Delande, Jeremie Houssineau +1
The Probability Hypothesis Density (PHD) and Cardinalized PHD (CPHD) filters are popular solutions to the multi-target tracking problem due to their low complexity and ability to e…
Smoothing and filtering with a class of outer measures
Jeremie Houssineau, Adrian N. Bishop
Filtering and smoothing with a generalised representation of uncertainty is considered. Here, uncertainty is represented using a class of outer measures. It is shown how this repre…
Spawning Models for the CPHD Filter
Daniel S. Bryant, Emmanuel D. Delande, Steven Gehly +3
In its classical form, the Cardinalized Probability Hypothesis Density (CPHD) filter does not model the appearance of new targets through spawning, yet there are applications for w…