Search Results for author:
Found 14 papers, 9 papers with code
Flexible Tails for Normalising Flows, with Application to the Modelling of Financial Return Data
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.
Measure Transport with Kernel Stein Discrepancy
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
One-shot Neural Architecture Search (NAS) aims to minimize the computational expense of discovering state-of-the-art models.
Ranked #39 on
Neural Architecture Search
on CIFAR-10
Distilling Importance Sampling for Likelihood Free Inference
The training data is "distilled" by using it to train an updated normalizing flow.
The Neural Moving Average Model for Scalable Variational Inference of State Space Models
Variational inference has had great success in scaling approximate Bayesian inference to big data by exploiting mini-batch training.
Black-Box Inference for Non-Linear Latent Force Models
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.
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 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
Most computational approaches to Bayesian experimental design require making posterior calculations repeatedly for a large number of potential designs and/or simulated datasets.
Computation
Black-Box Autoregressive Density Estimation for State-Space Models
State-space models (SSMs) provide a flexible framework for modelling time-series data.
Black-box Variational Inference for Stochastic Differential Equations
Parameter inference for stochastic differential equations is challenging due to the presence of a latent diffusion process.
Marginal sequential Monte Carlo for doubly intractable models
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.
An ABC interpretation of the multiple auxiliary variable method
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
Approximate Bayesian computation performs approximate inference for models where likelihood computations are expensive or impossible.
Computation
Lazier ABC
ABC algorithms involve a large number of simulations from the model of interest, which can be very computationally costly.
