Perturbations of Markov Chains
This chapter surveys progress on three related topics in perturbations of Markov chains: the
motivating question of when and how" perturbed" MCMC chains are developed, the …
motivating question of when and how" perturbed" MCMC chains are developed, the …
The block-Poisson estimator for optimally tuned exact subsampling MCMC
Abstract Speeding up Markov chain Monte Carlo (MCMC) for datasets with many
observations by data subsampling has recently received considerable attention. A pseudo …
observations by data subsampling has recently received considerable attention. A pseudo …
Spectral domain likelihoods for Bayesian inference in time-varying parameter models
Inference for locally stationary processes is often based on some local Whittle-type
approximation of the likelihood function defined in the frequency domain. The main reasons …
approximation of the likelihood function defined in the frequency domain. The main reasons …
Recursive variational Gaussian approximation with the Whittle likelihood for linear non-Gaussian state space models
BA Vu, D Gunawan, A Zammit-Mangion - arXiv preprint arXiv:2406.15998, 2024 - arxiv.org
Parameter inference for linear and non-Gaussian state space models is challenging
because the likelihood function contains an intractable integral over the latent state …
because the likelihood function contains an intractable integral over the latent state …
Spectral analysis for noisy Hawkes processes inference
A Bonnet, F Cheysson, MM Herrera… - arXiv preprint arXiv …, 2024 - arxiv.org
Classic estimation methods for Hawkes processes rely on the assumption that observed
event times are indeed a realisation of a Hawkes process, without considering any potential …
event times are indeed a realisation of a Hawkes process, without considering any potential …
Dynamic linear regression models for forecasting time series with semi long memory errors
Dynamic linear regression models forecast the values of a time series based on a linear
combination of a set of exogenous time series while incorporating a time series process for …
combination of a set of exogenous time series while incorporating a time series process for …
Inference of non-linear or imperfectly observed Hawkes processes
MM Herrera - 2024 - theses.hal.science
The Hawkes point process is a popular statistical tool to analyse temporal patterns. Modern
applications propose extensions of this model to account for specificities in each field of …
applications propose extensions of this model to account for specificities in each field of …
[PDF][PDF] Inference of non-linear or imperfectly observed Hawkes processes
G Ross - 2024 - migmtz.github.io
This chapter is a general introduction to Hawkes processes and the challenges explored in
this manuscript. After a succinct presentation of Hawkes processes with excitation, we …
this manuscript. After a succinct presentation of Hawkes processes with excitation, we …
Bayesian Analysis of Big Data via Subsampling Markov Chain Monte Carlo
Abstract Subsampling Markov chain Monte Carlo (MCMC) has emerged as an approach to
speed up Bayesian inference in the presence of large datasets. This article gives a brief and …
speed up Bayesian inference in the presence of large datasets. This article gives a brief and …