Richard Everitt

Richard Everitt

@bayesianstats.bsky.social

Posts about Bayesian statistics, Monte Carlo, etc from a Reader and Deputy Head of Department (T&L) at Warwick Statistics. Personal account. https://richardgeveritt.github.io/

3.3K followers2K following94 posts

We use a predictive-sequential loss function to fit deep generative forecasting models for data assimilation, with waste-free SMC for fitting the neural network + Bernstein-von Mises type theorem on the asymptotic behaviour of the generalised posterior, and applications to Lorenz96 and WeatherBench.

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In these examples we show how different MCMC moves can be used as part of the likelihood estimator, including slice sampling (as in original rare event ABC paper) and the preconditioned Crank-Nicolson Metropolis-Adjusted Langevin algorithm (in the new Lotka-Volterra example).

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The reviewers helped us a lot in developing this paper (thanks to them). Compared to earlier versions, we: (a) provide a heuristic argument that the computational cost of the algorithm scales cubicly with the dimension of the data (compared to exponentially for ABC-SMC);

(b) have a wider range of examples, looking at moving average models to show the method's use in model comparison, a Lotka-Volterra model for illustrating how the SMC sequence of distributions is helpful in exploring the parameter space, and an example of duplication-divergence models for networks.

A key idea is to use a reparametrisation trick, rewriting the simulator model as a deterministic function of the parameter and other random variables. Through this, we make use of the same likelihood estimator as used in the "particle MCMC version" of ABC-MCMC

link.springer.comA rare event approach to high-dimensional approximate Bayesian computationStatistics and Computing - Approximate Bayesian computation (ABC) methods permit approximate inference for intractable likelihoods when it is possible to simulate from the model. However, they...

I'm not sure one is supposed to repost praise, but I hope it can be forgiven in this case, since we had a lot indecision as to what this paper should look like before putting it out. In this situation it feels nice to have a vote of confidence!

Aki Vehtari@avehtari.bsky.social

Excellent paper and I fully agree with their conclusions. We had similar unpublished experimental results but ended up prioritizing other projects. This is a more extensive study of the issue and happy to see it published! @bayesianstats.bsky.social

Richard Everitt reposted

We use SMC to estimate the likelihood of SDE parameters, combining controlled SMC (to obtain low variance estimators) with numerical splitting schemes and diffusion bridges (to reduce the time-discretisation bias). We show how to use the approach in three different observation regimes.

"Annealing Strategies for Variance Reduction in Balance Heuristic Estimators" by Felipe Medina Aguayo and I is accepted in Stats and Computing. Multiple importance sampling uses multiple proposals to reduce variance. We use annealing to significantly further reduce variance. https://rdcu.be/eqe8j

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Speakers: Prof. Marta Blangiardo, Imperial Dr Hannah Christensen, Oxford Prof. Sophie Dabo-Niang, Lille Dr Jill Johnson, Sheffield Dr TJ McKinley, Exeter Dr Ben Swallow, St Andrews Dr William Thurston, Met Office

We infer prevalence and reproduction number of a disease over time, along with the reporting probability. The paper considers applications to HIV-1 in North Carolina and tuberculosis in Buenos Aires, and compares with alternative approaches that use only epidemiological or only genetic data.

Richard Everitt reposted