Elements of Causal Inference
1473 Kč
Sleva až 70% u třetiny knih
After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem.
Autor: | Peters, Jonas |
Nakladatel: | MIT Press |
Rok vydání: | 2018 |
Jazyk : | Angličtina |
Vazba: | Hardback |
Počet stran: | 288 |
Mohlo by se vám také líbit..
-
The Raven\'s Hat
Peters, Jonas
-
Agony of Eros
Han, Byung-Chul
-
Nonhuman Photography
Zylinska, Joanna
-
Translating Happiness
Lomas, Tim
-
Shanzhai
Han, Byung-Chul
-
German Philosophy - A Dialogue
Badiou, Alain
-
How to Write a Thesis
Eco, Umberto
-
Whole Earth Field Guide
Maniaque-Benton, Caroline
-
The Technological Singularity
Shanahan, Murray
-
Neuroplasticity
Costandi, Moheb
-
Communism for Kids
Adamczak, Bini
-
Learning From Las Vegas
Venturi, Robert
-
Deep Learning
Goodfellow, Ian
-
Sources of Power
Klein, Gary A.
-
Data Science
Kelleher, John D.
-
On Hitler's Mein Kampf
Koschorke, Albrecht