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Found 7 papers, 3 papers with code
Optimal quantisation of probability measures using maximum mean discrepancy
Several researchers have proposed minimisation of maximum mean discrepancy (MMD) as a method to quantise probability measures, i. e., to approximate a target distribution by a representative point set.
Stochastic Stein Discrepancies
Stein discrepancies (SDs) monitor convergence and non-convergence in approximate inference when exact integration and sampling are intractable.
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 $\{x_i\}_{i=1}^n$.
Measuring Sample Quality with Kernels
We develop a theory of weak convergence for KSDs based on Stein's method, demonstrate that commonly used KSDs fail to detect non-convergence even for Gaussian targets, and show that kernels with slowly decaying tails provably determine convergence for a large class of target distributions.
Measuring Sample Quality with Diffusions
Stein's method for measuring convergence to a continuous target distribution relies on an operator characterizing the target and Stein factor bounds on the solutions of an associated differential equation.
Measuring Sample Quality with Stein's Method
To improve the efficiency of Monte Carlo estimation, practitioners are turning to biased Markov chain Monte Carlo procedures that trade off asymptotic exactness for computational speed.
