[图书][B] Elements of causal inference: foundations and learning algorithms
A concise and self-contained introduction to causal inference, increasingly important in data
science and machine learning. The mathematization of causality is a relatively recent …
science and machine learning. The mathematization of causality is a relatively recent …
Higher order elicitability and Osband's principle
Higher order elicitability and Osband's principle Page 1 The Annals of Statistics 2016, Vol. 44,
No. 4, 1680–1707 DOI: 10.1214/16-AOS1439 © Institute of Mathematical Statistics, 2016 …
No. 4, 1680–1707 DOI: 10.1214/16-AOS1439 © Institute of Mathematical Statistics, 2016 …
Bayesian graphical models for modern biological applications
Y Ni, V Baladandayuthapani, M Vannucci… - Statistical Methods & …, 2022 - Springer
Graphical models are powerful tools that are regularly used to investigate complex
dependence structures in high-throughput biomedical datasets. They allow for holistic …
dependence structures in high-throughput biomedical datasets. They allow for holistic …
[图书][B] Probabilistic networks and expert systems: Exact computational methods for Bayesian networks
Winner of the 2002 DeGroot Prize. Probabilistic expert systems are graphical networks that
support the modelling of uncertainty and decisions in large complex domains, while …
support the modelling of uncertainty and decisions in large complex domains, while …
[图书][B] Introduction to graphical modelling
D Edwards - 2000 - books.google.com
Graphic modelling is a form of multivariate analysis that uses graphs to represent models.
These graphs display the structure of dependencies, both associational and causal …
These graphs display the structure of dependencies, both associational and causal …
Ancestral graph Markov models
T Richardson, P Spirtes - The Annals of Statistics, 2002 - projecteuclid.org
This paper introduces a class of graphical independence models that is closed under
marginalization and conditioning but that contains all DAG independence models. This class …
marginalization and conditioning but that contains all DAG independence models. This class …
[图书][B] Probabilistic conditional independence structures
M Studeny - 2006 - books.google.com
Conditional independence is a topic that lies between statistics and artificial intelligence.
Probabilistic Conditional Independence Structures provides the mathematical description of …
Probabilistic Conditional Independence Structures provides the mathematical description of …
Chain graph models and their causal interpretations
SL Lauritzen, TS Richardson - Journal of the Royal Statistical …, 2002 - academic.oup.com
Chain graphs are a natural generalization of directed acyclic graphs and undirected graphs.
However, the apparent simplicity of chain graphs belies the subtlety of the conditional …
However, the apparent simplicity of chain graphs belies the subtlety of the conditional …
Causality and graphical models in time series analysis
R Dahlhaus, M Eichler - Oxford Statistical Science Series, 2003 - books.google.com
Over the last few years there has been growing interest in graphical models and in particular
in those based on directed acyclic graphs as a general framework to describe and infer …
in those based on directed acyclic graphs as a general framework to describe and infer …
[图书][B] Unifying the mind: Cognitive representations as graphical models
D Danks - 2014 - books.google.com
A novel proposal that the unified nature of our cognition can be partially explained by a
cognitive architecture based on graphical models. Our ordinary, everyday thinking requires …
cognitive architecture based on graphical models. Our ordinary, everyday thinking requires …