# Context-specific independence in graphical log-linear models

@article{Nyman2016ContextspecificII, title={Context-specific independence in graphical log-linear models}, author={Henrik J. Nyman and Johan Pensar and Timo Koski and Jukka Corander}, journal={Computational Statistics}, year={2016}, volume={31}, pages={1493-1512} }

Log-linear models are the popular workhorses of analyzing contingency tables. A log-linear parameterization of an interaction model can be more expressive than a direct parameterization based on probabilities, leading to a powerful way of defining restrictions derived from marginal, conditional and context-specific independence. However, parameter estimation is often simpler under a direct parameterization, provided that the model enjoys certain decomposability properties. Here we introduce a…

## 21 Citations

Context-specific independencies in stratified chain regression graphical models

- Computer Science
- 2021

This work proposes a particular chain graphical model able to represent context-specific independencies, which are conditional independencies holding for particular values of the variables in the conditioning set through labeled arcs, and provides also the Markov properties able to describe marginal, conditional, and context- specific independencies from this new chain graph.

Context-specific independence in graphical models

- Computer Science
- 2014

The models introduced in this thesis enable the graphical representation of context-specific independencies, i.e. conditional independencies that hold only in a subset of the outcome space of the conditioning variables.

Log-linear models independence structure comparison

- Computer ScienceArXiv
- 2019

This work presents a measure for the direct comparison of the independence structures of log-linear models, inspired by the Hamming distance comparison method used in undirected graphical models, and can be efficiently computed in terms of the number of variables of the domain, and is proven to be a distance metric.

Structure learning of context-specific graphical models

- Computer Science
- 2016

Numerical experiments show that the increased flexibility of context-specific structures can more accurately emulate the dependence structure among the variables and thereby improve the predictive accuracy of the models.

Hierarchical Aitchison-Silvey models for incomplete binary sample spaces

- MathematicsJ. Multivar. Anal.
- 2022

Structure Learning of Contextual Markov Networks using Marginal Pseudo‐likelihood

- Computer Science
- 2017

The marginal pseudo‐likelihood as an analytically tractable criterion for general contextual Markov networks is introduced and is shown to yield a consistent structure estimator.

Context-Specific and Local Independence in Markovian Dependence Structures

- Computer ScienceDependence Logic
- 2016

Context-specific independence in different classes of Markovian probability models both for static and spatially or temporally organized variables, including Bayesian networks, Markov networks, and higher-order Markov chains are reviewed.

Context-specific independencies in hierarchical multinomial marginal models

- Computer ScienceStatistical Methods & Applications
- 2019

This paper considers the hierarchical multinomial marginal models and provides several original results about the representation of context-specific independencies through these models.

Context-specific Independence in Innovation Study

- Computer ScienceData Analysis and Applications 2
- 2019

This work focuses on the so called context-specific independence where the conditional independence holds only in a subspace of the outcome space and proposes a graphical representation of all the considered independencies taking advantages from the chain graph model.

Blankets Joint Posterior score for learning irregular Markov network structures

- Computer ScienceArXiv
- 2016

The Blankets Joint Posterior score is designed for computing the posterior probability of structures given data and can improve the learning process when the solution structure is irregular, which is a property present in many real-world networks.

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