9 citations · 19 across the 9 of their papers we have counts for
10 papers
Efficient Stochastic Optimisation via Sequential Monte Carlo
James Cuin, Davide Carbone, Yanbo Tang +1
The problem of optimising functions with intractable gradients frequently arises in machine learning and statistics, ranging from maximum marginal likelihood estimation procedures…
Post-reduction inference for confidence sets of models
Heather Battey, Daniel Garcia Rasines, Yanbo Tang
Sparsity in a regression context makes the model itself an object of interest, pointing to a confidence set of models as the appropriate presentation of evidence. A difficulty in a…
Monte Carlo and quasi-Monte Carlo integration for likelihood functions
Yanbo Tang
We compare the integration error of Monte Carlo (MC) and quasi-Monte Carlo (QMC) methods for approximating the normalizing constant of posterior distributions and certain marginal…
Variability of extragalactic X-ray jets on kiloparsec scales
Eileen T. Meyer, Aamil Shaik, Yanbo Tang +10
Unexpectedly strong X-ray emission from extragalactic radio jets on kiloparsec scales has been one of the major discoveries of Chandra, the only X-ray observatory capable of sub-ar…
On the Tightness of the Laplace Approximation for Statistical Inference
Blair Bilodeau, Yanbo Tang, Alex Stringer
Laplace's method is used to approximate intractable integrals in a statistical problems. The relative error rate of the approximation is not worse than . We provide th…
A Note on Monte Carlo Integration in High Dimensions
Yanbo Tang
Monte Carlo integration is a commonly used technique to compute intractable integrals and is typically thought to perform poorly for very high-dimensional integrals. To show that t…
Asymptotic Behaviour of the Modified Likelihood Root
Yanbo Tang, Nancy Reid
We examine the normal approximation of the modified likelihood root, an inferential tool from higher-order asymptotic theory, for the linear exponential and location-scale family.…
Laplace and Saddlepoint Approximations in High Dimensions
Yanbo Tang, Nancy Reid
We examine the behaviour of the Laplace and saddlepoint approximations in the high-dimensional setting, where the dimension of the model is allowed to increase with the number of o…
Stochastic Convergence Rates and Applications of Adaptive Quadrature in Bayesian Inference
Blair Bilodeau, Alex Stringer, Yanbo Tang
We provide the first stochastic convergence rates for a family of adaptive quadrature rules used to normalize the posterior distribution in Bayesian models. Our results apply to th…
General Behaviour of P-Values Under the Null and Alternative
Yanbo Tang, Radu Craiu, Lei Sun
Hypothesis testing results often rely on simple, yet important assumptions about the behaviour of the distribution of p-values under the null and the alternative. We examine tests…