73 citations · 232 across the 28 of their papers we have counts for
8 papers · 1 filter
A Bayesian Semiparametric Vector Multiplicative Error Model
Nicola Donelli, Stefano Peluso, Antonietta Mira
Interactions among multiple time series of positive random variables are crucial in diverse financial applications, from spillover effects to volatility interdependence. A popular…
Conditionally Gaussian Random Sequences for an Integrated Variance Estimator with Correlation between Noise and Returns
Stefano Peluso, Antonietta Mira, Pietro Muliere
Correlation between microstructure noise and latent financial logarithmic returns is an empirically relevant phenomenon with sound theoretical justification. With few notable excep…
Regularized Zero-Variance Control Variates
Leah F. South, Chris J. Oates, Antonietta Mira +1
Zero-variance control variates (ZV-CV) are a post-processing method to reduce the variance of Monte Carlo estimators of expectations using the derivatives of the log target. Once t…
ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation
Ritabrata Dutta, Marcel Schoengens, Lorenzo Pacchiardi +5
ABCpy is a highly modular scientific library for Approximate Bayesian Computation (ABC) written in Python. The main contribution of this paper is to document a software engineering…
Exploiting Multi-Core Architectures for Reduced-Variance Estimation with Intractable Likelihoods
Nial Friel, Antonietta Mira, Chris. J. Oates
Many popular statistical models for complex phenomena are intractable, in the sense that the likelihood function cannot easily be evaluated. Bayesian estimation in this setting rem…
Efficient computational strategies for doubly intractable problems with applications to Bayesian social networks
Alberto Caimo, Antonietta Mira
Powerful ideas recently appeared in the literature are adjusted and combined to design improved samplers for Bayesian exponential random graph models. Different forms of adaptive M…
Zero Variance Markov Chain Monte Carlo for Bayesian Estimators
Antonietta Mira, Reza Solgi, Daniele Imparato
Interest is in evaluating, by Markov chain Monte Carlo (MCMC) simulation, the expected value of a function with respect to a, possibly unnormalized, probability distribution. A gen…
Adaptive Multiple Importance Sampling
Jean-Marie Cornuet, Jean-Michel Marin, Antonietta Mira +1
The Adaptive Multiple Importance Sampling (AMIS) algorithm is aimed at an optimal recycling of past simulations in an iterated importance sampling scheme. The difference with earli…