Approximate Inference

Case-Based Approximate Reasoning (Repost)  eBooks & eLearning

Posted by DZ123 at Jan. 25, 2018
Case-Based Approximate Reasoning (Repost)

Eyke Hüllermeier, "Case-Based Approximate Reasoning"
English | 2007 | ISBN: 140205694X | PDF | pages: 383 | 5.3 mb

Case-Based Approximate Reasoning  eBooks & eLearning

Posted by tot167 at Oct. 8, 2007
Case-Based Approximate Reasoning

Eyke Hüllermeier, "Case-Based Approximate Reasoning"
Springer; 1 edition (March 5, 2007) | ISBN: 140205694X | 372 pages | PDF | 4,4 Mb

Case-based reasoning (CBR) has received a great deal of attention in recent years and has established itself as a core methodology in the field of artificial intelligence. The key idea of CBR is to tackle new problems by referring to similar problems that have already been solved in the past. More precisely, CBR proceeds from individual experiences in the form of cases. The generalization beyond these experiences typically relies on a kind of regularity assumption demanding that 'similar problems have similar solutions'.
Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies (repost)

Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies by Mark J. van der Laan
English | 2018 | ISBN: 3319653032 | 684 Pages | PDF | 9 MB

Simulation and Inference for Stochastic Processes with YUIMA  eBooks & eLearning

Posted by AvaxGenius at June 4, 2018
Simulation and Inference for Stochastic Processes with YUIMA

Simulation and Inference for Stochastic Processes with YUIMA: A Comprehensive R Framework for SDEs and Other Stochastic Processes By Stefano M. Iacus
English | PDF,EPUB | 2018 | 277 Pages | ISBN : 3319555677 | 12.31 MB

The YUIMA package is the first comprehensive R framework based on S4 classes and methods which allows for the simulation of stochastic differential equations driven by Wiener process, Lévy processes or fractional Brownian motion, as well as CARMA, COGARCH, and Point processes. The package performs various central statistical analyses such as quasi maximum likelihood estimation, adaptive Bayes estimation, structural change point analysis, hypotheses testing, asynchronous covariance estimation, lead-lag estimation, LASSO model selection, and so on.

Elements of Causal Inference: Foundations and Learning Algorithms  eBooks & eLearning

Posted by arundhati at April 1, 2018
Elements of Causal Inference: Foundations and Learning Algorithms

Jonas Peters,‎ Dominik Janzing, "Elements of Causal Inference: Foundations and Learning Algorithms"
2017 | ISBN-10: 0262037319 | 288 pages | PDF | 21 MB

Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference  eBooks & eLearning

Posted by Grev27 at March 30, 2018
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference

Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference by Cameron Davidson-Pilon
English | 2 Oct. 2015 | ISBN: 0133902838 | 250 Pages | PDF (True) | 19.4 MB

Deep Learning  eBooks & eLearning

Posted by arundhati at Nov. 11, 2016
Deep Learning

Ian Goodfellow, Yoshua Bengio, "Deep Learning (draft)"
2016 | ISBN-10: 0262035618 | 800 pages | PDF | 23 MB

Stanford University - Introduction to Artificial Intelligence [repost]  eBooks & eLearning

Posted by ParRus at Nov. 5, 2016
Stanford University - Introduction to Artificial Intelligence [repost]

Stanford University - Introduction to Artificial Intelligence
WEBRip | English | MP4 + PDF Guide | 640 x 360 | AVC ~250 kbps | 30 fps
AAC | 123 Kbps | 44.1 KHz | 2 channels | ~24 hours | 4.42 GB
Genre: eLearning Video / Science, Cybernetics, Probability

Online Introduction to Artificial Intelligence is based on Stanford CS221, Introduction to Artificial Intelligence. This class introduces students to the basics of Artificial Intelligence, which includes machine learning, probabilistic reasoning, robotics, and natural language processing. The objective of this class is to teach you modern AI. You learn about the basic techniques and tricks of the trade, at the same level we teach our Stanford students. We also aspire to excite you about the field of AI. Whether you are a seasoned professional, a college student, or a curious high school student - everyone can participate.

Learning Probabilistic Graphical Models in R  eBooks & eLearning

Posted by Grev27 at May 14, 2016
Learning Probabilistic Graphical Models in R

David Bellot, "Learning Probabilistic Graphical Models in R"
English | ISBN: 1784392057 | 2016 | PDF/EPUB/MOBI | 250 pages | 4 MB/7 MB/11 MB
Data Analysis and Approximate Models: Model Choice, Location-Scale, Analysis of Variance, Nonparametric Regression... (repost)

Data Analysis and Approximate Models: Model Choice, Location-Scale, Analysis of Variance, Nonparametric Regression and Image Analysis by Patrick Laurie Davies
English | 2014 | ISBN: 1482215861 | 320 pages | PDF | 11 MB