Recursive and sequential density estimation

Sequential nonparametric density estimation

Computational intelligence sequential Monte Carlos for recursive Bayesian estimation

Recursive Bayesian estimation using sequential Monte Carlos methods is a powerful numerical technique to understand latent dynamics of non-linear non-Gaussian dynamical systems. Classical sequential Monte Carlos suffer from weight degeneracy which is where the number of distinct particles collapse. Traditionally this is addressed by resampling, which effectively replaces high weight particles w...

A recursive procedure for density estimation on the binary hypercubeT1

Abstract: This paper describes a recursive estimation procedure for multivariate binary densities (probability distributions of vectors of Bernoulli random variables) using orthogonal expansions. For d covariates, there are 2 basis coefficients to estimate, which renders conventional approaches computationally prohibitive when d is large. However, for a wide class of densities that satisfy a ce...

Near-minimax recursive density estimation on the binary hypercube

This paper describes a recursive estimation procedure for multivariate binary densities using orthogonal expansions. For d covariates, there are 2 basis coefficients to estimate, which renders conventional approaches computationally prohibitive when d is large. However, for a wide class of densities that satisfy a certain sparsity condition, our estimator runs in probabilistic polynomial time a...

Distributed and Recursive Estimation

Estimation is a canonical problem in sensor networks. The intrinsic nature of sensor networks requires estimation algorithms based on sensor data to be distributed and recursive; such algorithms are studied in this chapter for the problem of (conditional) least squares estimation. The chapter is divided into three parts. In the first part, distributed and recursive estimation algorithms are dev...