LorenzoRimella

GraphFilter-GraphSmoother

Graph Filter and Graph Smoother are algorithms for approximate filtering and smoothing in high-dimensional factorial hidden Markov models. The approximation involves discarding, in a principled way, likelihood factors according a notion of locality in a factor graph associated with the emission distribution.

HiddenMarkovNeuralNetwork

An hybrid between HMM and Neural networks for sequential training and time series forecasting

Multinomial-Approximations-for-compartmental-models

Explanation on how to use the Multinomial Approximations for compartmental models proposed in "Inference in Stochastic Epidemic Models via Multinomial Approximations" by N. Whiteley and L. Rimella

PAL

Fast and consistent inference in compartmental models of epidemics using Poisson Approximate Likelihoods

Optimal_IBM_proposal

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 models is challenging due to the high-dimensional state-space of such models, which increases exponentially with population size. We consider sequential Monte Carlo algorithms for inference for individual-based epidemic models where we make direct observations of the state of a sample of individuals. Standard implementations, such as the bootstrap filter or the auxiliary particle filter are inefficient due to mismatch between the proposal distribution of the state and future observations. We develop new efficient proposal distributions that take account of future observations, leveraging the properties that (i) we can analytically calculate the optimal proposal distribution for a single individual given future observations and the future infection rate of that individual; and (ii) the dynamics of individuals are independent if we condition on their infection rates. Thus we construct estimates of the future infection rate for each individual, and then use an independent proposal for the state of each individual given this estimate. Empirical results show order of magnitude improvement in efficiency of the sequential Monte Carlo sampler for both SIS and SEIR models.

Build_Bayesian_NN_with_Pytorch

CV

epidemiology_workshop

GRDPG

Methodology Week 2

LorenzoRimella.github.io

LorenzoRimella/LorenzoRimella.github.io

LorenzoRimella/CV

LorenzoRimella/epidemiology_workshop

LorenzoRimella/SimBa-CL

Simulation Based Composite Likelihood

LorenzoRimella/PAL

Fast and consistent inference in compartmental models of epidemics using Poisson Approximate Likelihoods

LorenzoRimella/Build_Bayesian_NN_with_Pytorch

LorenzoRimella/GRDPG

Methodology Week 2

LorenzoRimella/SMC2-ILM

Stochastic modelling and parameters inference through SMC^2 for AMR bacteria

LorenzoRimella/Optimal_IBM_proposal

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 models is challenging due to the high-dimensional state-space of such models, which increases exponentially with population size. We consider sequential Monte Carlo algorithms for inference for individual-based epidemic models where we make direct observations of the state of a sample of individuals. Standard implementations, such as the bootstrap filter or the auxiliary particle filter are inefficient due to mismatch between the proposal distribution of the state and future observations. We develop new efficient proposal distributions that take account of future observations, leveraging the properties that (i) we can analytically calculate the optimal proposal distribution for a single individual given future observations and the future infection rate of that individual; and (ii) the dynamics of individuals are independent if we condition on their infection rates. Thus we construct estimates of the future infection rate for each individual, and then use an independent proposal for the state of each individual given this estimate. Empirical results show order of magnitude improvement in efficiency of the sequential Monte Carlo sampler for both SIS and SEIR models.

LorenzoRimella/site_generator

LorenzoRimella/GraphFilter-GraphSmoother

Graph Filter and Graph Smoother are algorithms for approximate filtering and smoothing in high-dimensional factorial hidden Markov models. The approximation involves discarding, in a principled way, likelihood factors according a notion of locality in a factor graph associated with the emission distribution.

LorenzoRimella/HiddenMarkovNeuralNetwork

An hybrid between HMM and Neural networks for sequential training and time series forecasting

LorenzoRimella/Multinomial-Approximations-for-compartmental-models

Explanation on how to use the Multinomial Approximations for compartmental models proposed in "Inference in Stochastic Epidemic Models via Multinomial Approximations" by N. Whiteley and L. Rimella

LorenzoRimella/RGeode