Artistic Models

Inference in Hidden Markov Models  eBooks & eLearning

Posted by step778 at Aug. 9, 2018
Inference in Hidden Markov Models

Olivier Cappé, Eric Moulines, Tobias Ryden, "Inference in Hidden Markov Models"
2007 | pages: 668 | ISBN: 0387402640 | PDF | 4,6 mb

Analytical Models for Decision Making  eBooks & eLearning

Posted by step778 at Aug. 9, 2018
Analytical Models for Decision Making

Colin Sanderson, Reinhold Gruen, "Analytical Models for Decision Making"
2006 | pages: 248 | ISBN: 0335218458 | PDF | 7,5 mb
Frontiers of Pleasure : Models of Aesthetic Response in Archaic and Classical Greek Thought

Frontiers of Pleasure : Models of Aesthetic Response in Archaic and Classical Greek Thought by Anastasia-Erasmia Peponi
English | August 2, 2012 | ISBN: 019979832X | PDF | 224 pages | 1.7 MB

Flying Scale Models – September 2018  Magazines

Posted by Inshuf at Aug. 9, 2018
Flying Scale Models – September 2018

Flying Scale Models – September 2018
English | 68 pages | True PDF | 18.4 MB

Active Particles, Volume 1: Advances in Theory, Models, and Applications [Repost]  eBooks & eLearning

Posted by ChrisRedfield at Aug. 8, 2018
Active Particles, Volume 1: Advances in Theory, Models, and Applications [Repost]

Nicola Bellomo, Pierre Degond, Eitan Tadmor - Active Particles, Volume 1: Advances in Theory, Models, and Applications
Published: 2017-04-11 | ISBN: 3319499947, 3319842951 | PDF | 402 pages | 24.52 MB

Improving Machine Learning with Continuous Learning Models  eBooks & eLearning

Posted by naag at Aug. 8, 2018
Improving Machine Learning with Continuous Learning Models

Improving Machine Learning with Continuous Learning Models
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1.5 Hours | 222 MB
Genre: eLearning | Language: English

NoSQL Data Models: Trends and Challenges  eBooks & eLearning

Posted by roxul at Aug. 7, 2018
NoSQL Data Models: Trends and Challenges

Olivier Pivert, "NoSQL Data Models: Trends and Challenges"
English | ISBN: 1786303647 | 2018 | 278 pages | PDF | 6 MB

Models for Discrete Longitudinal Data (Repost)  eBooks & eLearning

Posted by AvaxGenius at Aug. 7, 2018
Models for Discrete Longitudinal Data (Repost)

Models for Discrete Longitudinal Data by Geert Molenberghs
English | PDF | 2005 | 679 Pages | ISBN : 0387251448 | 5.52 MB

This book provides a comprehensive treatment on modeling approaches for non-Gaussian repeated measures, possibly subject to incompleteness. The authors begin with models for the full marginal distribution of the outcome vector. This allows model fitting to be based on maximum likelihood principles, immediately implying inferential tools for all parameters in the models.

Linear Models for Optimal Test Design (Repost)  eBooks & eLearning

Posted by AvaxGenius at Aug. 7, 2018
Linear Models for Optimal Test Design (Repost)

Linear Models for Optimal Test Design by Wim J. van der Linden
English | PDF | 2005 | 421 Pages | ISBN : 0387202722 | 2.72 MB

This book begins with a reflection on the history of test design–the core activity of all educational and psychological testing. It then presents a standard language for modeling test design problems as instances of multi-objective constrained optimization. The main portion of the book discusses test design models for a large variety of problems from the daily practice of testing, and illustrates their use with the help of numerous empirical examples.
Plane Answers to Complex Questions: The Theory of Linear Models, Third Edition (Repost)

Plane Answers to Complex Questions: The Theory of Linear Models, Third Edition by Ronald Christensen
English | PDF | 2002 | 493 Pages | ISBN : 1441929711 | 34.7 MB

The third edition of Plane Answers includes fundamental changes in how some aspects of the theory are handled. Chapter 1 includes a new section that introduces generalized linear models. Primarily, this provides a defini­ tion so as to allow comments on how aspects of linear model theory extend to generalized linear models. For years I have been unhappy with the concept of estimability. Just because you cannot get a linear unbiased estimate of something does not mean you cannot estimate it.