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Found 6 papers, 0 papers with code
Likelihood-free Model Choice for Simulator-based Models with the Jensen--Shannon Divergence
Choice of appropriate structure and parametric dimension of a model in the light of data has a rich history in statistical research, where the first seminal approaches were developed in 1970s, such as the Akaike's and Schwarz's model scoring criteria that were inspired by information theory and embodied the rationale called Occam's razor.
Nonparametric likelihood-free inference with Jensen-Shannon divergence for simulator-based models with categorical output
Likelihood-free inference for simulator-based statistical models has recently attracted a surge of interest, both in the machine learning and statistics communities.
Testing for Causality in Continuous Time Bayesian Network Models of High-Frequency Data
It is calibrated to market data and by the new causality measure it performs better than older models.
Context-specific independence in graphical log-linear models
Log-linear models are the popular workhorses of analyzing contingency tables.
Labeled Directed Acyclic Graphs: a generalization of context-specific independence in directed graphical models
We introduce a novel class of labeled directed acyclic graph (LDAG) models for finite sets of discrete variables.
Stratified Graphical Models - Context-Specific Independence in Graphical Models
Theory of graphical models has matured over more than three decades to provide the backbone for several classes of models that are used in a myriad of applications such as genetic mapping of diseases, credit risk evaluation, reliability and computer security, etc.
