4 citations · 12 across the 9 of their papers we have counts for
9 papers
Scalable calibration of individual-based epidemic models through categorical approximations
Lorenzo Rimella, Nick Whiteley, Chris Jewell +2
Traditional compartmental models capture population-level dynamics but fail to characterize individual-level risk. The computational cost of exact likelihood evaluation for partial…
Disclosure risk assessment with Bayesian non-parametric hierarchical modelling
Marco Battiston, Lorenzo Rimella
Micro and survey datasets often contain private information about individuals, like their health status, income or political preferences. Previous studies have shown that, even aft…
Simulation Based Composite Likelihood
Lorenzo Rimella, Chris Jewell, Paul Fearnhead
Inference for high-dimensional hidden Markov models is challenging due to the exponential-in-dimension computational cost of calculating the likelihood. To address this issue, we i…
A State-Space Perspective on Modelling and Inference for Online Skill Rating
Samuel Duffield, Samuel Power, Lorenzo Rimella
We summarise popular methods used for skill rating in competitive sports, along with their inferential paradigms and introduce new approaches based on sequential Monte Carlo and di…
Approximating optimal SMC proposal distributions in individual-based epidemic models
Lorenzo Rimella, Christopher Jewell, Paul Fearnhead
Many epidemic models are naturally defined as individual-based models: where we track the state of each individual within a susceptible population. Inference for individual-based m…
Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods
Michael Whitehouse, Nick Whiteley, Lorenzo Rimella
Addressing the challenge of scaling-up epidemiological inference to complex and heterogeneous models, we introduce Poisson Approximate Likelihood (PAL) methods. In contrast to the…
Inference in Stochastic Epidemic Models via Multinomial Approximations
Nick Whiteley, Lorenzo Rimella
We introduce a new method for inference in stochastic epidemic models which uses recursive multinomial approximations to integrate over unobserved variables and thus circumvent lik…
Hidden Markov Neural Networks
Lorenzo Rimella, Nick Whiteley
We define an evolving in-time Bayesian neural network called a Hidden Markov Neural Network, which addresses the crucial challenge in time-series forecasting and continual learning…
Exploiting locality in high-dimensional factorial hidden Markov models
Lorenzo Rimella, Nick Whiteley
We propose algorithms for approximate filtering and smoothing in high-dimensional Factorial hidden Markov models. The approximation involves discarding, in a principled way, likeli…