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Found 20 papers, 13 papers with code
Robust and Conjugate Gaussian Process Regression
To enable closed form conditioning, a common assumption in Gaussian process (GP) regression is independent and identically distributed Gaussian observation noise.
Bayesian Numerical Integration with Neural Networks
Bayesian probabilistic numerical methods for numerical integration offer significant advantages over their non-Bayesian counterparts: they can encode prior information about the integrand, and can quantify uncertainty over estimates of an integral.
Meta-learning Control Variates: Variance Reduction with Limited Data
Control variates can be a powerful tool to reduce the variance of Monte Carlo estimators, but constructing effective control variates can be challenging when the number of samples is small.
Robust and Scalable Bayesian Online Changepoint Detection
This paper proposes an online, provably robust, and scalable Bayesian approach for changepoint detection.
Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference
Likelihood-free inference methods typically make use of a distance between simulated and real data.
Towards Healing the Blindness of Score Matching
Score-based divergences have been widely used in machine learning and statistics applications.
Generalised Bayesian Inference for Discrete Intractable Likelihood
Discrete state spaces represent a major computational challenge to statistical inference, since the computation of normalisation constants requires summation over large or possibly infinite sets, which can be impractical.
Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap
Simulator-based models are models for which the likelihood is intractable but simulation of synthetic data is possible.
ProbNum: Probabilistic Numerics in Python
Probabilistic numerical methods (PNMs) solve numerical problems via probabilistic inference.
Composite Goodness-of-fit Tests with Kernels
Model misspecification can create significant challenges for the implementation of probabilistic models, and this has led to development of a range of robust methods which directly account for this issue.
Robust Generalised Bayesian Inference for Intractable Likelihoods
Generalised Bayesian inference updates prior beliefs using a loss function, rather than a likelihood, and can therefore be used to confer robustness against possible mis-specification of the likelihood.
The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks
Our approach constructs a prior distribution for the parameters of the network, called a ridgelet prior, that approximates the posited Gaussian process in the output space of the network.
Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization
Control variates are a well-established tool to reduce the variance of Monte Carlo estimators.
Bayesian Probabilistic Numerical Integration with Tree-Based Models
The advantages and disadvantages of this new methodology are highlighted on a set of benchmark tests including the Genz functions, and on a Bayesian survey design problem.
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness
In this setting, an important theoretical question of practial relevance is how accurate the Gaussian process approximations will be given the difficulty of the problem, our model and the extent of the misspecification.
Stein Point Markov Chain Monte Carlo
Stein Points are a class of algorithms for this task, which proceed by sequentially minimising a Stein discrepancy between the empirical measure and the target and, hence, require the solution of a non-convex optimisation problem to obtain each new point.
Stein Points
An important task in computational statistics and machine learning is to approximate a posterior distribution $p(x)$ with an empirical measure supported on a set of representative points $\{x_i\}_{i=1}^n$.
Bayesian Quadrature for Multiple Related Integrals
This allows users to represent uncertainty in a more faithful manner and, as a by-product, provide increased numerical efficiency.
Probabilistic Integration: A Role in Statistical Computation?
A research frontier has emerged in scientific computation, wherein numerical error is regarded as a source of epistemic uncertainty that can be modelled.
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees
There is renewed interest in formulating integration as an inference problem, motivated by obtaining a full distribution over numerical error that can be propagated through subsequent computation.
