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  Browse All Reviews > Mathematics Of Computing (G) > Probability And Statistics (G.3) > Statistical Computing (G.3...)  
 
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  1-10 of 183 Reviews about "Statistical Computing (G.3...)": Date Reviewed
   A domain theory for statistical probabilistic programming
Vákár M., Kammar O., Staton S.  Proceedings of the ACM on Programming Languages 3(POPL): 1-29, 2019. Type: Article

On the one hand, a statistical programming language is similar to a traditional programming language, but with libraries providing statistical functions. Examples are Mathematica, MATLAB, and the omnipresent R. On the other hand, probabilistic pro...

Aug 6 2020
   Data exploration using example-based methods
Lissandrini M., Mottin D., Palpanas T., Velegrakis Y.,  Morgan&Claypool Publishers, San Rafael, CA, 2019. 164 pp. Type: Book (978-1-681734-55-2)

This book provides a comprehensive overview and description of a broad range of search algorithms. Because the authors are experts at example-based searching methods, the book discusses the application of example-based approaches to several applic...

Nov 18 2019
  Learning representation for multi-view data analysis: models and applications
Ding Z., Zhao H., Fu Y.,  Springer International Publishing, New York, NY, 2019. 268 pp. Type: Book (978-3-030007-33-1)

Ding et al.’s Learning representation for multi-view data analysis not only provides cutting-edge research on multi-view data representation and analysis, but also provides several visual applications and practical challenges, includi...

May 7 2019
  Introductory biostatistics (2nd ed.)
Le C., Eberly L.,  Wiley Publishing, Hoboken, NJ, 2016. 616 pp. Type: Book (978-0-470905-40-1)

Le and Eberly’s Introductory biostatistics is in fact more than introductory. Not only do the authors painstakingly clarify the fundamentals of the topic, but they also provide sufficient material for an advanced undergraduate or post...

Apr 19 2019
  Basic elements of computational statistics
Härdle W., Okhrin O., Okhrin Y.,  Springer International Publishing, New York, NY, 2017. 305 pp. Type: Book (978-3-319553-35-1)

Researchers frequently use statistics to analyze their results. Statistical analysis is a vital tool to confirm or reject hypotheses. In this context, R, one of the most famous programs used for data analysis and statistics, is a powerful, time-sa...

Jan 24 2019
  Introduction to deep learning using R: a step-by-step guide to learning and implementing deep learning models using R
Beysolow T.,  Apress, New York, NY, 2017. 227 pp. Type: Book (978-1-484227-33-6)

This is the era of machine intelligence. The domain of machine learning, and in particular deep learning, has evolved rapidly, and we have started to develop and use real-life applications from this evolution. The book serves the purpose of provid...

Jul 11 2018
  Game theory for data science: eliciting truthful information
Faltings B., Radanovic G.,  Morgan & Claypool Publishers, San Rafael, CA, 2017. 152 pp. Type: Book (978-1-627057-29-5)

Quality is crucial for any use of collected data. In addition to more traditional methods of data cleansing for data warehousing, new kinds of data provided by people have become important. To get truthful information from the answering individual...

Apr 12 2018
  Beginning data science in R: data analysis, visualization, and modelling for the data scientist
Mailund T.,  Apress, New York, NY, 2017. 352 pp. Type: Book (978-1-484226-70-4)

Data science has existed for 40 years, but is increasingly receiving more attention since large amounts of structured and unstructured data (big data) are collected every day within industry, the healthcare sector, the environment, and so on. Ever...

Mar 23 2018
  Statistical analysis of proteomics, metabolomics, and lipidomics data using mass spectrometry
Datta S., Mertens B.,  Springer International Publishing, New York, NY, 2016. 295 pp. Type: Book (978-3-319458-07-6)

This is a collection of technical papers dedicated to mass spectrometric data processing. Mass spectrometry is becoming a standard of studying structures and features of biomolecules in the genomic era. The ultimate goal is to fully investigate bi...

Jan 17 2018
  Machine learning using R
Ramasubramanian K., Singh A.,  Apress, New York, NY, 2016. 566 pp. Type: Book (978-1-484223-33-8)

Karthik Ramasubramanian and Abhishek Singh, experts in data science and business analytics, have written a comprehensive reference book on machine learning using R. This book covers a relevant and hot topic in today’s digital world....

Oct 26 2017
 
 
 
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