288 citations · 772 across the 29 of their papers we have counts for
8 papers · 1 filter
Sampling with censored data: a practical guide
Pedro L. Ramos, Daniel C. F. Guzman, Alex L. Mota +3
In this review, we present a simple guide for researchers to obtain pseudo-random samples with censored data. We focus our attention on the most common types of censored data, such…
Cooperative Parallel Particle Filters for online model selection and applications to Urban Mobility
Luca Martino, Jesse Read, Victor Elvira +1
We design a sequential Monte Carlo scheme for the dual purpose of Bayesian inference and model selection. We consider the application context of urban mobility, where several modal…
Effective Sample Size for Importance Sampling based on discrepancy measures
L. Martino, V. Elvira, F. Louzada
The Effective Sample Size (ESS) is an important measure of efficiency of Monte Carlo methods such as Markov Chain Monte Carlo (MCMC) and Importance Sampling (IS) techniques. In the…
Parallel Metropolis chains with cooperative adaptation
L. Martino, V. Elvira, D. Luengo +1
Monte Carlo methods, such as Markov chain Monte Carlo (MCMC) algorithms, have become very popular in signal processing over the last years. In this work, we introduce a novel MCMC…
Adaptive Rejection Sampling with fixed number of nodes
L. Martino, F. Louzada
The adaptive rejection sampling (ARS) algorithm is a universal random generator for drawing samples efficiently from a univariate log-concave target probability density function (p…
Issues in the Multiple Try Metropolis mixing
L. Martino, F. Louzada
The multiple Try Metropolis (MTM) algorithm is an advanced MCMC technique based on drawing and testing several candidates at each iteration of the algorithm. One of them is selecte…
Orthogonal parallel MCMC methods for sampling and optimization
L. Martino, V. Elvira, D. Luengo +2
Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. A well-known class of MC methods are Markov…
BayesDccGarch - An Implementation of Multivariate GARCH DCC Models
Jose A. Fioruci, Ricardo S. Ehlers, Francisco Louzada
Multivariate GARCH models are important tools to describe the dynamics of multivariate times series of financial returns. Nevertheless, these models have been much less used in pra…