1 citations · 1 across the 12 of their papers we have counts for
12 papers
Non-Uniform Random Scans in Gibbs Sampling and CAVI
Sam Power
Gibbs sampling and coordinate ascent variational inference (CAVI) are two basic coordinate-wise methods for statistical computation. Recent analyses under strong log-concavity esta…
A Uniformly Efficient Rejection Sampler for the Multivariate Expectile-Based Distribution
Sam Power
The multivariate expectile-based distribution is a `spiked' perturbation of a multivariate Gaussian distribution, proposed by Arbel et al. in 2023. The rejection sampler proposed f…
Uniform efficiency of the Ulrich-Wood sampler for the von Mises-Fisher distribution
Sam Power
The standard approach to stochastic simulation from the von Mises-Fisher distribution is a rejection sampler proposed by Ulrich and Wood. This note provides a theoretical justifica…
A Complexity Bound for the Kent-Ganeiber-Mardia Sampler for the Bingham Distribution
Sam Power
The Bingham distribution is a family of antipodally symmetric distributions on the unit sphere, characterised by an exponential-of-quadratic change of measure with respect to the u…
Robustness of random-walk Metropolis for steep potentials
Sam Power
In Markov chain Monte Carlo sampling, light-tailed target distributions present something of a poisoned chalice: their light tails offer good confinement, and tend to imply good mi…
The sharp one-dimensional convex sub-Gaussian comparison constant
Damek Davis, Sam Power
Let be an integrable real random variable with mean zero and two-sided sub-Gaussian tail for all . We determine the smallest consta…
Some aspects of robustness in modern Markov Chain Monte Carlo
Sam Power, Giorgos Vasdekis
Markov Chain Monte Carlo (MCMC) is a flexible approach to approximate sampling from intractable probability distributions, with a rich theoretical foundation and comprising a wealt…
Distributional Training Data Attribution: What do Influence Functions Sample?
Bruno Mlodozeniec, Isaac Reid, Sam Power +4
Randomness is an unavoidable part of training deep learning models, yet something that traditional training data attribution algorithms fail to rigorously account for. They ignore…
Analysis of Multiple-try Metropolis via Poincaré inequalities
Rocco Caprio, Sam Power, Andi Q. Wang
We study the Multiple-try Metropolis algorithm using the framework of Poincaré inequalities. We describe the Multiple-try Metropolis as an auxiliary variable implementation of a re…
A New Proof of Sub-Gaussian Norm Concentration Inequality
Zishun Liu, Sam Power, Yongxin Chen
We present a new method for proving the norm concentration inequality of sub-Gaussian variables. Our proof is based on an averaged version of the moment generating function, termed…
Towards practical PDMP sampling: Metropolis adjustments, locally adaptive step-sizes, and NUTS-based time lengths
Augustin Chevallier, Sam Power, Matthew Sutton
Piecewise-Deterministic Markov Processes (PDMPs) hold significant promise for sampling from complex probability distributions. However, their practical implementation is hindered b…
Weak Poincaré inequality comparisons for ideal and hybrid slice sampling
Sam Power, Daniel Rudolf, Björn Sprungk +1
Using the framework of weak Poincaré inequalities, we provide a general comparison between Hybrid and Ideal Slice Sampling in terms of their corresponding Dirichlet forms. In parti…