27 citations · 39 across the 6 of their papers we have counts for
6 papers
Copula-type Estimators for Flexible Multivariate Density Modeling using Mixtures
Minh-Ngoc Tran, Paolo Giordani, Xiuyan Mun +2
Copulas are popular as models for multivariate dependence because they allow the marginal densities and the joint dependence to be modeled separately. However, they usually require…
Auxiliary Particle filtering within adaptive Metropolis-Hastings Sampling
Michael Pitt, Ralph Silva, Paolo Giordani +1
Our article deals with Bayesian inference for a general state space model with the simulated likelihood computed by the particle filter. We show empirically that the partially or f…
A copula based approach to adaptive sampling
Ralph Silva, Robert Kohn, Paolo Giordani +1
Our article is concerned with adaptive sampling schemes for Bayesian inference that update the proposal densities using previous iterates. We introduce a copula based proposal dens…
Particle filtering within adaptive Metropolis Hastings sampling
Ralph Silva, Paolo Giordani, Robert Kohn +1
We show that it is feasible to carry out exact Bayesian inference for non-Gaussian state space models using an adaptive Metropolis Hastings sampling scheme with the likelihood appr…
Flexible Multivariate Density Estimation with Marginal Adaptation
Paolo Giordani, Xiuyan Mun, Robert Kohn
Our article addresses the problem of flexibly estimating a multivariate density while also attempting to estimate its marginals correctly. We do so by proposing two new estimators…
Adaptive Independent Metropolis-Hastings by Fast Estimation of Mixtures of Normals
P. Giordani, R. Kohn
We construct an adaptive independent Metropolis-Hastings sampler that uses a mixture of normals as a proposal distribution. To take full advantage of the potential of adaptive samp…