4 citations · 7 across the 18 of their papers we have counts for
20 papers
Overcoming Model Misspecification in Bayesian Inference of Molecular Signalling Networks
Yinghan Li, Heishiro Kanagawa, Francesca Romana Crucinio +2
Bayesian inference of molecular signalling networks usually relies on tractability of the marginal likelihood, enabling the set of possible networks to be efficiently explored. As…
Preservation of Log-Concavity and Convergence of Wasserstein-Fisher-Rao Gradient Flows
Francesca Romana Crucinio, Sahani Pathiraja
We study the convergence of Wasserstein-Fisher-Rao (WFR) gradient flows for sampling from probability distributions known up to a normalisation constant. By combining Wasserstein t…
Properties and limitations of geometric tempering for gradient flow dynamics
Francesca Romana Crucinio, Sahani Pathiraja
We consider the problem of sampling from a probability distribution . It is well known that this can be written as an optimisation problem over the space of probability distribu…
An operator splitting analysis of Wasserstein--Fisher--Rao gradient flows
Francesca Romana Crucinio, Sahani Pathiraja
Wasserstein-Fisher-Rao (WFR) gradient flows have been recently proposed as a powerful sampling tool that combines the advantages of pure Wasserstein (W) and pure Fisher-Rao (FR) gr…
A note on the unique properties of the Kullback--Leibler divergence for sampling via gradient flows
Francesca Romana Crucinio
We consider the problem of sampling from a probability distribution which admits a density w.r.t. a dominating measure. It is well known that this can be written as an optimisa…
Sequential Monte Carlo approximations of Wasserstein--Fisher--Rao gradient flows
Francesca R. Crucinio, Sahani Pathiraja
We consider the problem of sampling from a probability distribution . It is well known that this can be written as an optimisation problem over the space of probability distribu…
A mirror descent approach to maximum likelihood estimation in latent variable models
Francesca R. Crucinio
We introduce an approach based on mirror descent and sequential Monte Carlo (SMC) to perform joint parameter inference and posterior estimation in latent variable models. This appr…
Solving Fredholm Integral Equations of the Second Kind via Wasserstein Gradient Flows
Francesca R. Crucinio, Adam M. Johansen
Motivated by a recent method for approximate solution of Fredholm equations of the first kind, we develop a corresponding method for a class of Fredholm equations of the \emph{seco…
Proximal Interacting Particle Langevin Algorithms
Paula Cordero Encinar, Francesca R. Crucinio, O. Deniz Akyildiz
We introduce a class of algorithms, termed proximal interacting particle Langevin algorithms (PIPLA), for inference and learning in latent variable models whose joint probability d…
Towards a turnkey approach to unbiased Monte Carlo estimation of smooth functions of expectations
Nicolas Chopin, Francesca R. Crucinio, Sumeetpal S. Singh
Given a smooth function , we develop a general approach to turn Monte Carlo samples with expectation into an unbiased estimate of . Specifically, we develop estimators…
A connection between Tempering and Entropic Mirror Descent
Nicolas Chopin, Francesca R. Crucinio, Anna Korba
This paper explores the connections between tempering (for Sequential Monte Carlo; SMC) and entropic mirror descent to sample from a target probability distribution whose unnormali…
Properties of Marginal Sequential Monte Carlo Methods
Francesca R. Crucinio, Adam M. Johansen
We provide a framework which admits a number of ``marginal'' sequential Monte Carlo (SMC) algorithms as particular cases -- including the marginal particle filter [Klaas et al., 20…
Interacting Particle Langevin Algorithm for Maximum Marginal Likelihood Estimation
Ö. Deniz Akyildiz, Francesca Romana Crucinio, Mark Girolami +2
We develop a class of interacting particle systems for implementing a maximum marginal likelihood estimation (MMLE) procedure to estimate the parameters of a latent variable model.…
Optimal Scaling Results for Moreau-Yosida Metropolis-adjusted Langevin Algorithms
Francesca R. Crucinio, Alain Durmus, Pablo Jiménez +1
We consider a recently proposed class of MCMC methods which uses proximity maps instead of gradients to build proposal mechanisms which can be employed for both differentiable and…
A divide and conquer sequential Monte Carlo approach to high dimensional filtering
Francesca R. Crucinio, Adam M. Johansen
We propose a divide-and-conquer approach to filtering which decomposes the state variable into low-dimensional components to which standard particle filtering tools can be successf…
Solving Fredholm Integral Equations of the First Kind via Wasserstein Gradient Flows
Francesca R. Crucinio, Valentin De Bortoli, Arnaud Doucet +1
Solving Fredholm equations of the first kind is crucial in many areas of the applied sciences. In this work we adopt a probabilistic and variational point of view by considering a…
The divide-and-conquer sequential Monte Carlo algorithm: theoretical properties and limit theorems
Juan Kuntz, Francesca R. Crucinio, Adam M. Johansen
We provide a comprehensive characterisation of the theoretical properties of the divide-and-conquer sequential Monte Carlo (DaC-SMC) algorithm. We firmly establish it as a well-fou…
Product-form estimators: exploiting independence to scale up Monte Carlo
Juan Kuntz, Francesca R. Crucinio, Adam M. Johansen
We introduce a class of Monte Carlo estimators that aim to overcome the rapid growth of variance with dimension often observed for standard estimators by exploiting the target's in…
A Particle Method for Solving Fredholm Equations of the First Kind
Francesca R Crucinio, Arnaud Doucet, Adam M Johansen
Fredholm integral equations of the first kind are the prototypical example of ill-posed linear inverse problems. They model, among other things, reconstruction of distorted noisy o…
Markov Chain Monte Carlo sampling for conditional tests: A link between permutation tests and algebraic statistics
Roberto Fontana, Francesca Romana Crucinio
We consider conditional tests for non-negative discrete exponential families. We develop two Markov Chain Monte Carlo (MCMC) algorithms which allow us to sample from the conditiona…