Nonlinear non-Gaussian state-space models are ubiquitous in statistics, econometrics,
information engineering and signal processing. Particle methods, also known as Sequential …

ABSTRACT Statisticians often use Monte Carlo methods to approximate probability
distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential …

We consider the generic problem of performing sequential Bayesian inference in a state
space model with observation process y, state process x and fixed parameter θ. An idealized …

State-space models can be used to incorporate subject knowledge on the underlying
dynamics of a time series by the introduction of a latent Markov state process. A user can …

The paper proposes a self-exciting asset pricing model that takes into account co-jumps
between prices and volatility and self-exciting jump clustering. We employ a Bayesian …

In state–space models, smoothing refers to the task of estimating a latent stochastic process
given noisy measurements related to the process. We propose an unbiased estimator of …

In this article we study the leverage effect in cryptocurrency markets using a stochastic
volatility model with simultaneous and correlated jumps in returns and volatility. We estimate …

The Bayes factor is a widely used criterion in model comparison and its logarithm is a
difference of out-of-sample predictive scores under the logarithmic scoring rule. However …

Massively parallel desktop computing capabilities now well within the reach of individual
academics modify the environment for posterior simulation in fundamental and potentially …

We propose a density-tempered marginalized sequential Monte Carlo (SMC) sampler, a
new class of samplers for full Bayesian inference of general state-space models. The …