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09/15/2023
On the Forgetting of Particle Filters
We study the forgetting properties of the particle filter when its state...
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07/07/2023
On the convergence of dynamic implementations of Hamiltonian Monte Carlo and No U-Turn Samplers
There is substantial empirical evidence about the success of dynamic imp...
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06/27/2023
Simulating counterfactuals
Counterfactual inference considers a hypothetical intervention in a para...
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05/27/2022
Conditional particle filters with bridge backward sampling
The performance of the conditional particle filter (CPF) with backward s...
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03/18/2022
On resampling schemes for particle filters with weakly informative observations
We consider particle filters with weakly informative observations (or `p...
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01/21/2021
bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R
We present an R package bssm for Bayesian non-linear/non-Gaussian state ...
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06/26/2020
Conditional particle filters with diffuse initial distributions
Conditional particle filters (CPFs) are powerful smoothing algorithms fo...
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05/05/2020
Hierarchical spatial log Gaussian Cox process model for replicated point patterns
We propose a conditional log Gaussian Cox process (LGCP) model to invest...
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02/01/2019
On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction
Approximate Bayesian computation allows for inference of complicated pro...
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02/01/2019
On the use of ABC-MCMC with inflated tolerance and post-correction
Approximate Bayesian computation (ABC) allows for inference of complicat...
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01/08/2019
Graphical model inference: Sequential Monte Carlo meets deterministic approximations
Approximate inference in probabilistic graphical models (PGMs) can be gr...
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07/26/2018
Unbiased inference for discretely observed hidden Markov model diffusions
We develop an importance sampling (IS) type estimator for Bayesian joint...
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06/15/2018
