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Found 5 papers, 4 papers with code
A State-Space Perspective on Modelling and Inference for Online Skill Rating
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 discrete hidden Markov models.
Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods
In contrast to the popular ODE approach to compartmental modelling, in which a large population limit is used to motivate a deterministic model, PALs are derived from approximate filtering equations for finite-population, stochastic compartmental models, and the large population limit drives consistency of maximum PAL estimators.
Inference in Stochastic Epidemic Models via Multinomial Approximations
We introduce a new method for inference in stochastic epidemic models which uses recursive multinomial approximations to integrate over unobserved variables and thus circumvent likelihood intractability.
Dynamic Bayesian Neural Networks
We define an evolving in time Bayesian neural network called a Hidden Markov neural network.
Exploiting locality in high-dimensional factorial hidden Markov models
We propose algorithms for approximate filtering and smoothing in high-dimensional Factorial hidden Markov models.
