5 matching results for "statistics":
Submitted Feb 07, 2017 to Science Courses and Tutorials This course offers an introduction to the finite sample analysis of high dimensional statistical methods. The goal is to present various proof techniques for stateoftheart methods in regression, matrix estimation and principal component analysis (PCA) as well as optimality guarantees. The course ends with research questions that are currently open. Taught by Prof. Philippe Rigollet.

Submitted Jan 16, 2017 to Science Blogs This blog is devoted to statistical thinking and its impact on science and everyday life. Emphasis is given to maximizing the use of information, avoiding statistical pitfalls, describing problems caused by the frequentist approach to statistical inference, describing advantages of Bayesian and likelihood methods, and discussing intended and unintended differences between statistics and data science. I'll also cover regression modeling strategies, clinical trials, and drug evaluation. By Frank Harrell.

Submitted Jan 13, 2017 to Science Journals and News Established in 1996, the Journal of Statistical Software publishes articles, book reviews, code snippets, and software reviews on the subject of statistical software and algorithms. The contents are freely available online. For both articles and code snippets the source code is published along with the paper.

Submitted Jan 13, 2017 to Science Research Articles Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuousvariable models through Markov chain Monte Carlo methods such as the NoUTurn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory BroydenFletcherGoldfarbShanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimizationbased inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.

Submitted Jan 07, 2017 to Science Blogs RBloggers is an aggregator website where you will find daily news and tutorials about the R statistical computing software for statistics, data science, and more, contributed by over 573 bloggers.

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