BibSonomy bookmarks for /tag/statistical

hypothesis testing - What's the formula for the Benjamini-Hochberg adjusted p-value? - Cross Validated

becker — 2022-06-13T20:52:06+02:00

https://stats.stackexchange.com/questions/238458/whats-the-formula-for-the-benjamini-hochberg-adjusted-p-value

Large scale correlation mining: fundamental performance limits

becker — 2021-01-07T18:16:09+01:00

http://web.eecs.umich.edu/~hero/Preprints/hero_duke_SAHD15.pdf

Statistical Significance Tests for Comparing Machine Learning Algorithms

becker — 2020-05-02T22:24:38+02:00

https://machinelearningmastery.com/statistical-significance-tests-for-comparing-machine-learning-algorithms/

False Positives, FWER, and FDR Explained | Less Likely

becker — 2019-05-31T01:57:46+02:00

https://lesslikely.com/statistics/multiplicity-explained/

statistical significance between dependent Spearman coefficients

schwemmlein — 2018-09-11T21:52:46+02:00

Calculating statistical significance between dependent Spearman coefficients - Cross Validated

sigtest.pdf

schwemmlein — 2018-09-03T13:55:38+02:00

https://cs.stanford.edu/people/wmorgan/sigtest.pdf

Performance Evaluation for Learning Algorithms

becker — 2018-03-08T10:46:07+01:00

http://www.site.uottawa.ca/~nat/Talks/ICMLA-Tutorial.pptx

Evaluating Learning Algorithms: A Classification Perspective: Amazon.de: Nathalie Japkowicz: Fremdsprachige Bücher

becker — 2018-03-08T10:42:27+01:00

https://www.amazon.de/Evaluating-Learning-Algorithms-Classification-Perspective/dp/1107653118

Analysebaum.pdf

becker — 2018-02-13T18:43:52+01:00

http://methodenlehre.sowi.uni-mainz.de/download/Lehre/SS2012/SemDA/Persike/Analysebaum.pdf

CLARK GRUBB

schwemmlein — 2017-11-04T20:33:01+01:00

http://clarkgrubb.com/

Computational Investigation of Translationese

schwemmlein — 2015-09-22T23:24:16+02:00

Computational Investigation of Translationese

Scientific method: Statistical errors : Nature News & Comment

gwotto — 2015-07-09T13:00:30+02:00

http://www.nature.com/news/scientific-method-statistical-errors-1.14700

RStudio - Shiny

akpe — 2013-08-27T13:44:29+02:00

Shiny makes it super simple for R users like you to turn analyses into interactive web applications that anyone can use. Let your users choose input parameters using friendly controls like sliders, drop-downs, and text fields. Easily incorporate any number of outputs like plots, tables, and summaries.

Tutorial: Building 'Shiny' Applications with R

akpe — 2013-08-27T13:44:29+02:00

http://rstudio.github.io/shiny/tutorial/#welcome

R-bloggers | R news & tutorials from the web

akpe — 2013-08-27T13:44:29+02:00

http://www.r-bloggers.com/

Learning Theory (Formal, Computational or Statistical)

sidyr — 2013-05-09T12:04:10+02:00

http://vserver1.cscs.lsa.umich.edu/~crshalizi/notabene/learning-theory.html

Practical Statistical Relational Learning - videolectures.net

schwemmlein — 2013-03-04T15:14:16+01:00

The tutorial will be composed of three parts: # Foundational areas. The first part will consist of a brief introduction to each of the four foundational areas of SRL: logical inference, inductive logic programming, probabilistic inference, and statistical learning. Obviously, in the short time available no attempt will be made to comprehensively survey these areas; rather, the focus will be on providing the key concepts and techniques required for the subsequent parts. For example, the logical inference part will focus on the basics of satisfiability testing, and the probabilistic/statistical parts on Markov networks. The duration of this part will be approximately two hours (half hour per subtopic). # Putting the pieces together. The second part will introduce the key ideas in SRL and survey major approaches, using Markov logic as the unifying framework. It will present state-of-the-art algorithms for statistical relational learning and inference, and give an overview of the Alchemy open-source software. This part will essentially consist of putting together the pieces introduced in the first part. Its duration will be approximately an hour. # Applications. The third and final part will describe how to efficiently develop state-of-the-art non-i.i.d. applications in various areas, including: hypertext classification, link-based information retrieval, information extraction and integration, natural language processing, social network modeling, computational biology, and ubiquitous computing. This part will also include practical tips on using SRL, Markov logic and Alchemy - the kind of information that is seldom found in research papers, but is key to developing successful applications. The duration of this part will be approximately an hour.

2nd Warsaw School on Statistical Physics

ks-plugin-devel — 2013-02-02T14:47:05+01:00

2nd Warsaw School on Statistical Physics

Statistical programming with R, Part 3: Reusable and object-oriented programming

vivion — 2013-01-28T17:02:19+01:00

http://www.ibm.com/developerworks/linux/library/l-r3/index.html

Lise Getoor's Homepage

schwemmlein — 2012-07-17T17:36:41+02:00

http://www.cs.umd.edu/~getoor/