Search Results for author: Dennis Prangle

Found 14 papers, 9 papers with code

Flexible Tails for Normalising Flows, with Application to the Modelling of Financial Return Data

1 code implementation1 Nov 2023 Tennessee Hickling, Dennis Prangle

We propose a transformation capable of altering the tail properties of a distribution, motivated by extreme value theory, which can be used as a layer in a normalizing flow to approximate multivariate heavy tailed distributions.

Normalising Flows

Measure Transport with Kernel Stein Discrepancy

1 code implementation22 Oct 2020 Matthew A. Fisher, Tui Nolan, Matthew M. Graham, Dennis Prangle, Chris J. Oates

Measure transport underpins several recent algorithms for posterior approximation in the Bayesian context, wherein a transport map is sought to minimise the Kullback--Leibler divergence (KLD) from the posterior to the approximation.

Bonsai-Net: One-Shot Neural Architecture Search via Differentiable Pruners

1 code implementation12 Jun 2020 Rob Geada, Dennis Prangle, Andrew Stephen McGough

One-shot Neural Architecture Search (NAS) aims to minimize the computational expense of discovering state-of-the-art models.

Neural Architecture Search

The Neural Moving Average Model for Scalable Variational Inference of State Space Models

1 code implementation2 Oct 2019 Tom Ryder, Dennis Prangle, Andrew Golightly, Isaac Matthews

Variational inference has had great success in scaling approximate Bayesian inference to big data by exploiting mini-batch training.

Bayesian Inference Normalising Flows +3

Black-Box Inference for Non-Linear Latent Force Models

no code implementations21 Jun 2019 Wil O. C. Ward, Tom Ryder, Dennis Prangle, Mauricio A. Álvarez

Latent force models are systems whereby there is a mechanistic model describing the dynamics of the system state, with some unknown forcing term that is approximated with a Gaussian process.

Gaussian Processes Variational Inference

Ensemble MCMC: Accelerating Pseudo-Marginal MCMC for State Space Models using the Ensemble Kalman Filter

1 code implementation5 Jun 2019 Christopher Drovandi, Richard G. Everitt, Andrew Golightly, Dennis Prangle

Particle Markov chain Monte Carlo (pMCMC) is now a popular method for performing Bayesian statistical inference on challenging state space models (SSMs) with unknown static parameters.

Computation Methodology

Bayesian experimental design without posterior calculations: an adversarial approach

2 code implementations11 Apr 2019 Sophie Harbisher, Colin S Gillespie, Dennis Prangle

Most computational approaches to Bayesian experimental design require making posterior calculations repeatedly for a large number of potential designs and/or simulated datasets.


Black-box Variational Inference for Stochastic Differential Equations

2 code implementations ICML 2018 Thomas Ryder, Andrew Golightly, A. Stephen McGough, Dennis Prangle

Parameter inference for stochastic differential equations is challenging due to the presence of a latent diffusion process.

Variational Inference

Marginal sequential Monte Carlo for doubly intractable models

no code implementations12 Oct 2017 Richard G. Everitt, Dennis Prangle, Philip Maybank, Mark Bell

Bayesian inference for models that have an intractable partition function is known as a doubly intractable problem, where standard Monte Carlo methods are not applicable.

Bayesian Inference

An ABC interpretation of the multiple auxiliary variable method

no code implementations27 Apr 2016 Dennis Prangle, Richard G. Everitt

We show that the auxiliary variable method (M{\o}ller et al., 2006; Murray et al., 2006) for inference of Markov random fields can be viewed as an approximate Bayesian computation method for likelihood estimation.

Adapting the ABC distance function

1 code implementation3 Jul 2015 Dennis Prangle

Approximate Bayesian computation performs approximate inference for models where likelihood computations are expensive or impossible.


Lazier ABC

no code implementations21 Jan 2015 Dennis Prangle

ABC algorithms involve a large number of simulations from the model of interest, which can be very computationally costly.

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