This article serves as an introduction and survey for economists to the field of sequential
Monte Carlo methods which are also known as particle filters. Sequential Monte Carlo …

This thesis contributes to the field of Bayesian machine learning. Familiarity with most of the
material in Bishop [2007], MacKay [2003] and Hastie et al.[2009] would thus be convenient …

In this paper we review sequential Monte Carlo (SMC) methods, or particle filters (PF), with
special emphasis on its potential applications in financial time series analysis and …

Exact Gaussian process (GP) regression has \calO(N^3) runtime for data size N, making it
intractable for large N. Many algorithms for improving GP scaling approximate the …

We design a particle interpretation of Feynman-Kac measures on path spaces based on a
backward Markovian representation combined with a traditional mean field particle …

Based on the principles of importance sampling and resampling, sequential Monte Carlo
(SMC) encompasses a large set of powerful techniques dealing with complex stochastic …

This paper discusses particle filtering in general hidden Markov models (HMMs) and
presents novel theoretical results on the long-term stability of bootstrap-type particle filters …

Particle filtering and smoothing algorithms approximate posterior state distributions with a
set of samples drawn from those distributions. Conventionally, samples from the joint …

Abstract Exact Gaussian Process (GP) regression has O (N^ 3) runtime for data size N,
making it intractable for large N. Advances in GP scaling have not been extended to the …

HF‐band radio‐frequency identification (RFID) is a robust identification system that is rarely
influenced by objects in the robot activity area or by illumination conditions. An HF‐band …