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10/22/2020
Measure Transport with Kernel Stein Discrepancy
Measure transport underpins several recent algorithms for posterior appr...
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10/08/2019
Distilling importance sampling
The two main approaches to Bayesian inference are sampling and optimisat...
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10/02/2019
Scalable approximate inference for state space models with normalising flows
By exploiting mini-batch stochastic gradient optimisation, variational i...
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06/21/2019
Variational Bridge Constructs for Grey Box Modelling with Gaussian Processes
This paper introduces a method for inference of heterogeneous dynamical ...
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06/05/2019
Ensemble MCMC: Accelerating Pseudo-Marginal MCMC for State Space Models using the Ensemble Kalman Filter
Particle Markov chain Monte Carlo (pMCMC) is now a popular method for pe...
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04/11/2019
Bayesian optimal design using stochastic gradient optimisation and Fisher information gain
Finding high dimensional designs is increasingly important in applicatio...
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04/11/2019
High dimensional optimal design using stochastic gradient optimisation and Fisher information gain
Finding high dimensional designs is increasingly important in applicatio...
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01/14/2019
Optimality Criteria for Probabilistic Numerical Methods
It is well understood that Bayesian decision theory and average case ana...
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11/20/2018
Black-Box Autoregressive Density Estimation for State-Space Models
State-space models (SSMs) provide a flexible framework for modelling tim...
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02/09/2018
Black-box Variational Inference for Stochastic Differential Equations
Parameter inference for stochastic differential equations is challenging...
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10/12/2017
Marginal sequential Monte Carlo for doubly intractable models
Bayesian inference for models that have an intractable partition functio...
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04/27/2016
An ABC interpretation of the multiple auxiliary variable method
We show that the auxiliary variable method (Møller et al., 2006; Murray ...
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01/21/2015