{"results":[{"id":"arxiv_2504.11299","title":"Efficient and Stable Multi-Dimensional Kolmogorov-Smirnov Distance","authors":[{"name":"Peter Matthew Jacobs"},{"name":"Foad Namjoo"},{"name":"Jeff M. Phillips"}],"abstract":"We revisit extending the Kolmogorov-Smirnov distance between probability distributions to the multidimensional setting and make new arguments about the proper way to approach this generalization. Our proposed formulation maximizes the difference over orthogonal dominating rectangular ranges (d-sided rectangles in R^d), and is an integral probability metric. We also prove that the distance between a distribution and a sample from the distribution converges to 0 as the sample size grows, and bound this rate. Moreover, we show that one can, up to this same approximation error, compute the distance efficiently in 4 or fewer dimensions; specifically the runtime is near-linear in the size of the sample needed for that error. With this, we derive a delta-precision two-sample hypothesis test using this distance. Finally, we show these metric and approximation properties do not hold for other popular variants.","source":"arXiv","year":2025,"language":"en","subjects":["stat.CO","cs.CG","cs.LG"],"url":"https://arxiv.org/abs/2504.11299","pdf_url":"https://arxiv.org/pdf/2504.11299","is_open_access":true,"published_at":"2025-04-15T15:42:49Z","score":69},{"id":"arxiv_2401.02518","title":"Perfecting MCMC Sampling: Recipes and Reservations","authors":[{"name":"Radu V. Craiu"},{"name":"Xiao-Li Meng"}],"abstract":"This review paper is intended for the Handbook of Markov chain Monte Carlo's second edition. The authors will be grateful for any suggestions that could perfect it.","source":"arXiv","year":2024,"language":"en","subjects":["stat.CO"],"url":"https://arxiv.org/abs/2401.02518","pdf_url":"https://arxiv.org/pdf/2401.02518","is_open_access":true,"published_at":"2024-01-04T20:03:30Z","score":68},{"id":"arxiv_2410.00496","title":"Grand Challenges in Bayesian Computation","authors":[{"name":"Anirban Bhattacharya"},{"name":"Antonio Linero"},{"name":"Chris. J. Oates"}],"abstract":"This article appeared in the September 2024 issue (Vol. 31, No. 3) of the Bulletin of the International Society for Bayesian Analysis (ISBA).","source":"arXiv","year":2024,"language":"en","subjects":["stat.CO"],"url":"https://arxiv.org/abs/2410.00496","pdf_url":"https://arxiv.org/pdf/2410.00496","is_open_access":true,"published_at":"2024-10-01T08:27:38Z","score":68},{"id":"arxiv_2405.07286","title":"A Short Note on a Flexible Cholesky Parameterization of Correlation Matrices","authors":[{"name":"Sean Pinkney"}],"abstract":"We propose a Cholesky factor parameterization of correlation matrices that facilitates a priori restrictions on the correlation matrix. It is a smooth and differentiable transform that allows additional boundary constraints on the correlation values. Our particular motivation is random sampling under positivity constraints on the space of correlation matrices using MCMC methods.","source":"arXiv","year":2024,"language":"en","subjects":["stat.CO"],"url":"https://arxiv.org/abs/2405.07286","pdf_url":"https://arxiv.org/pdf/2405.07286","is_open_access":true,"published_at":"2024-05-12T13:55:40Z","score":68},{"id":"arxiv_2303.12185","title":"Sampling from a Gaussian distribution conditioned on the level set of a piecewise affine, continuous function","authors":[{"name":"Jesse Windle"}],"abstract":"We consider how to use Hamiltonian Monte Carlo to sample from a distribution whose log-density is piecewise quadratic, conditioned on the sample lying on the level set of a piecewise affine, continuous function.","source":"arXiv","year":2023,"language":"en","subjects":["stat.CO"],"url":"https://arxiv.org/abs/2303.12185","pdf_url":"https://arxiv.org/pdf/2303.12185","is_open_access":true,"published_at":"2023-03-21T20:30:56Z","score":67},{"id":"arxiv_2203.06559","title":"A Brief Introduction to Redis","authors":[{"name":"Dirk Eddelbuettel"}],"abstract":"This note provides a brief introduction to Redis highlighting its usefulness in multi-lingual statistical computing.","source":"arXiv","year":2022,"language":"en","subjects":["stat.CO"],"url":"https://arxiv.org/abs/2203.06559","pdf_url":"https://arxiv.org/pdf/2203.06559","is_open_access":true,"published_at":"2022-03-13T03:47:24Z","score":66},{"id":"arxiv_2203.08323","title":"Redis for Market Monitoring","authors":[{"name":"Dirk Eddelbuettel"}],"abstract":"This note shows how to use Redis cache (near-)real-time market data, and utilise its publish/subscribe (\"pub/sub\") facility to distribute the data.","source":"arXiv","year":2022,"language":"en","subjects":["stat.CO"],"url":"https://arxiv.org/abs/2203.08323","pdf_url":"https://arxiv.org/pdf/2203.08323","is_open_access":true,"published_at":"2022-03-16T00:06:09Z","score":66},{"id":"arxiv_2109.06075","title":"Minimum Discrepancy Methods in Uncertainty Quantification","authors":[{"name":"Chris J. Oates"}],"abstract":"The lectures were prepared for the École Thématique sur les Incertitudes en Calcul Scientifique (ETICS) in September 2021.","source":"arXiv","year":2021,"language":"en","subjects":["stat.CO"],"url":"https://arxiv.org/abs/2109.06075","pdf_url":"https://arxiv.org/pdf/2109.06075","is_open_access":true,"published_at":"2021-09-13T15:51:22Z","score":65},{"id":"arxiv_2105.02952","title":"Comments on: A Gibbs sampler for a class of random convex polytopes","authors":[{"name":"Kentaro Hoffman"},{"name":"Jan Hannig"},{"name":"Kai Zhang"}],"abstract":"In this comment we discuss relative strengths and weaknesses of simplex and Dirichlet Dempster-Shafer inference as applied to multi-resolution tests of independence.","source":"arXiv","year":2021,"language":"en","subjects":["stat.CO"],"doi":"10.1080/01621459.2021.1950002","url":"https://arxiv.org/abs/2105.02952","pdf_url":"https://arxiv.org/pdf/2105.02952","is_open_access":true,"published_at":"2021-04-15T19:39:34Z","score":65},{"id":"arxiv_1812.10612","title":"Sampling on the sphere from $f(x) \\propto x^TAx$","authors":[{"name":"Richard Arnold"}],"abstract":"A method for drawing random samples of unit vectors $x$ in $R^p$ with density proportional to $x^TAx$ where $A$ is a symmetric, positive definite matrix. Includes an R function which implements the method.","source":"arXiv","year":2018,"language":"en","subjects":["stat.CO"],"url":"https://arxiv.org/abs/1812.10612","pdf_url":"https://arxiv.org/pdf/1812.10612","is_open_access":true,"published_at":"2018-12-27T03:40:12Z","score":62},{"id":"arxiv_1702.08251","title":"Hessian corrections to Hybrid Monte Carlo","authors":[{"name":"Thomas House"}],"abstract":"A method for the introduction of second-order derivatives of the log likelihood into HMC algorithms is introduced, which does not require the Hessian to be evaluated at each leapfrog step but only at the start and end of trajectories.","source":"arXiv","year":2017,"language":"en","subjects":["stat.CO"],"url":"https://arxiv.org/abs/1702.08251","pdf_url":"https://arxiv.org/pdf/1702.08251","is_open_access":true,"published_at":"2017-02-27T12:12:46Z","score":61},{"id":"arxiv_1703.08627","title":"Random sampling of Latin squares via binary contingency tables and probabilistic divide-and-conquer","authors":[{"name":"Stephen DeSalvo"}],"abstract":"We demonstrate a novel approach for the random sampling of Latin squares of order~$n$ via probabilistic divide-and-conquer. The algorithm divides the entries of the table modulo powers of $2$, and samples a corresponding binary contingency table at each level. The sampling distribution is based on the Boltzmann sampling heuristic, along with probabilistic divide-and-conquer.","source":"arXiv","year":2017,"language":"en","subjects":["stat.CO"],"url":"https://arxiv.org/abs/1703.08627","pdf_url":"https://arxiv.org/pdf/1703.08627","is_open_access":true,"published_at":"2017-03-24T23:49:33Z","score":61},{"id":"arxiv_1510.04923","title":"Simpler Online Updates for Arbitrary-Order Central Moments","authors":[{"name":"Xiangrui Meng"}],"abstract":"Statistical moments are widely used in descriptive statistics. Therefore efficient and numerically stable implementations are important in practice. Pebay [1] derives online update formulas for arbitrary-order central moments. We present a simpler version that is also easier to implement.","source":"arXiv","year":2015,"language":"en","subjects":["stat.CO"],"url":"https://arxiv.org/abs/1510.04923","pdf_url":"https://arxiv.org/pdf/1510.04923","is_open_access":true,"published_at":"2015-10-16T15:42:25Z","score":59},{"id":"arxiv_1306.6684","title":"Supplement to \"Markov Chain Monte Carlo Based on Deterministic Transformations\"","authors":[{"name":"Somak Dutta"},{"name":"Sourabh Bhattacharya"}],"abstract":"This is a supplement to the article \"Markov Chain Monte Carlo Based on Deterministic Transformations\" available at http://arxiv.org/abs/1106.5850","source":"arXiv","year":2013,"language":"en","subjects":["stat.CO"],"url":"https://arxiv.org/abs/1306.6684","pdf_url":"https://arxiv.org/pdf/1306.6684","is_open_access":true,"published_at":"2013-06-28T00:10:19Z","score":57},{"id":"arxiv_1204.4148","title":"Algorithm for multivariate data standardization up to third moment","authors":[{"name":"Vadim Asnin"}],"abstract":"An algorithm for transforming multivariate data to a form with normalized first, second and third moments is presented.","source":"arXiv","year":2012,"language":"en","subjects":["stat.CO","stat.ME","stat.OT"],"url":"https://arxiv.org/abs/1204.4148","pdf_url":"https://arxiv.org/pdf/1204.4148","is_open_access":true,"published_at":"2012-04-18T17:46:32Z","score":56},{"id":"arxiv_1011.1745","title":"Online Expectation-Maximisation","authors":[{"name":"Olivier Cappé"}],"abstract":"Tutorial chapter on the Online EM algorithm to appear in the volume 'Mixtures' edited by Kerrie Mengersen, Mike Titterington and Christian P. Robert.","source":"arXiv","year":2010,"language":"en","subjects":["stat.CO"],"url":"https://arxiv.org/abs/1011.1745","pdf_url":"https://arxiv.org/pdf/1011.1745","is_open_access":true,"published_at":"2010-11-08T10:01:57Z","score":54},{"id":"arxiv_1005.2355","title":"On a Multiplicative Algorithm for Computing Bayesian D-optimal Designs","authors":[{"name":"Yaming Yu"}],"abstract":"We use the minorization-maximization principle (Lange, Hunter and Yang 2000) to establish the monotonicity of a multiplicative algorithm for computing Bayesian D-optimal designs.  This proves a conjecture of Dette, Pepelyshev and Zhigljavsky (2008).","source":"arXiv","year":2010,"language":"en","subjects":["stat.CO","stat.ME"],"url":"https://arxiv.org/abs/1005.2355","pdf_url":"https://arxiv.org/pdf/1005.2355","is_open_access":true,"published_at":"2010-05-13T15:53:10Z","score":54},{"id":"arxiv_0905.4131","title":"Maximum Likelihood Estimation for Markov Chains","authors":[{"name":"Iuliana Teodorescu"}],"abstract":"A new approach for optimal estimation of Markov chains with sparse transition matrices is presented.","source":"arXiv","year":2009,"language":"en","subjects":["stat.CO","math.ST"],"url":"https://arxiv.org/abs/0905.4131","pdf_url":"https://arxiv.org/pdf/0905.4131","is_open_access":true,"published_at":"2009-05-26T08:20:50Z","score":53},{"id":"arxiv_0902.4117","title":"A Gibbs Sampling Alternative to Reversible Jump MCMC","authors":[{"name":"Stephen G. Walker"}],"abstract":"This note presents a simple and elegant sampler which could be used as an alternative to the reversible jump MCMC methodology.","source":"arXiv","year":2009,"language":"en","subjects":["stat.CO"],"url":"https://arxiv.org/abs/0902.4117","pdf_url":"https://arxiv.org/pdf/0902.4117","is_open_access":true,"published_at":"2009-02-24T11:02:31Z","score":53},{"id":"crossref_10.1002/pssa.2210200129","title":"Interdiffusion in the FeNi, NiCo, and FeCo systems","authors":[{"name":"T. Ustad"},{"name":"H. Sørum"}],"abstract":"","source":"CrossRef","year":1973,"language":"en","subjects":null,"doi":"10.1002/pssa.2210200129","url":"https://doi.org/10.1002/pssa.2210200129","is_open_access":true,"citations":73,"published_at":"","score":52.19}],"total":1126848,"page":1,"page_size":20,"sources":["CrossRef","arXiv"],"query":"stat.CO"}