The estimation problem for stochastic parametric nonlinear dynamical models is recognized
to be challenging. The main difficulty is the intractability of the likelihood function and the …

A pseudo-marginal sequential Monte Carlo online smoothing algorithm Page 1 Bernoulli 28(4),
2022, 2606–2633 https://doi.org/10.3150/21-BEJ1431 A pseudo-marginal sequential Monte …

It is well known that ignoring the presence of stochastic disturbances in the identification of
stochastic Wiener models leads to asymptotically biased estimators. On the other hand …

Being the most classical generative model for serial data, state-space models (SSM) are
fundamental in AI and statistical machine learning. In SSM, any form of parameter learning …

General state-space models (SSMs) are widely used in statistical machine learning and are
among the most classical generative models for sequential time-series data. SSMs …

The paper obtains analytical results for the asymptotic properties of Model Selection Criteria–
widely used in practice–for a general family of hidden Markov models (HMMs), thereby …

This article proposes a new Sequential Monte Carlo algorithm to perform online estimation
in the context of state space models when either the transition density of the latent state or …

In many scenarios, a state-space model depends on a parameter which needs to be inferred
from data. Using stochastic gradient search and the optimal filter first-order derivatives, the …

In many applications, a state-space model depends on a parameter which needs to be
inferred from data in an online manner. In the maximum likelihood approach, this can be …

In this thesis, we develop new models and algorithms to solve deep learning tasks on
sequential data problems, with the perspective of tackling the pitfalls of current approaches …