Results 1 to 10 of about 18,333,402 (334)
A new look at the statistical model identification
IEEE Transactions on Automatic Control, 1974 The history of the development of statistical hypothesis testing in time series analysis is reviewed briefly and it is pointed out that the hypothesis testing procedure is not adequately defined as the procedure for statistical model identification.
H. Akaike
exaly +2 more sources
Technometrics, 2021 A machine learning system, in general, learns from the environment, but statistical machine learning programs (systems) learn from the data. This chapter presents techniques for statistical machine learning using Support Vector Machines (SVM) to ...
Yuhai Wu
semanticscholar +1 more source
Seaborn: Statistical Data Visualization
Journal of Open Source Software, 2021 seaborn is a library for making statistical graphics in Python. It provides a high-level interface to matplotlib and integrates closely with pandas data structures. Functions in the seaborn library expose a declarative, dataset-oriented API that makes it
Michael L. Waskom
semanticscholar +1 more source
Manual for Theoretical Chemistry, 2021 Professor Chen Ning Yang has made seminal and influential contributions in many different areas in theoretical physics. This talk focuses on his contributions in statistical mechanics, a field in which Professor Yang has held a continual interest for ...
Uwe-Jens Wiese, Albert Einstein
semanticscholar +1 more source
performance: An R Package for Assessment, Comparison and Testing of Statistical Models
Journal of Open Source Software, 2021 A crucial part of statistical analysis is evaluating a model's quality and fit, or performance. During analysis, especially with regression models, investigating the fit of models to data also often involves selecting the best fitting model amongst many ...
D. Lüdecke +4 more
semanticscholar +1 more source
An introduction to statistical learning with applications in R
Statistical Theory and Related Fields, 2021 The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.
Fariha Sohil +2 more
semanticscholar +1 more source
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation [PDF]
Conference on Empirical Methods in Natural Language Processing, 2014 In this paper, we propose a novel neural network model called RNN Encoder‐ Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the ...
Kyunghyun Cho +6 more
semanticscholar +1 more source
Bayesian Statistical Pragmatism [PDF]
, 2011 Discussion of "Statistical Inference: The Big Picture" by R. E. Kass [arXiv:1106.2895]Comment: Published in at http://dx.doi.org/10.1214/11-STS337C the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http:
Gelman, Andrew
core +3 more sources
Rehabilitating Statistical Evidence [PDF]
, 2021 Recently, the practice of deciding legal cases on purely statistical evidence has been widely criticised. Many feel uncomfortable with finding someone guilty on the basis of bare probabilities, even though the chance of error might be stupendously small.
Ross, Lewis
core
Statistical mechanics of complex networks [PDF]
arXiv.org, 2001 The emergence of order in natural systems is a constant source of inspiration for both physical and biological sciences. While the spatial order characterizing for example the crystals has been the basis of many advances in contemporary physics, most ...
R. Albert, A. Barabási
semanticscholar +1 more source