Χρήση αιτιατής και εξαρτησιακής πρότερης γνώσης για την κατασκευή αιτιατών μοντέλων

 
This item is provided by the institution :
University of Crete
Repository :
E-Locus Institutional Repository
see the original item page
in the repository's web site and access all digital files if the item*
share




2013 (EN)
Incorporating Causal and Associative Prior Knowledge when Learning Causal Models
Χρήση αιτιατής και εξαρτησιακής πρότερης γνώσης για την κατασκευή αιτιατών μοντέλων

Μπορμπουδάκης, Γεώργιος Νικόλαος

Τσαμαρδινός, Ιωάννης

Causal graphical models, such as Causal Bayesian Networks, are widely used to model the dependency structure, as well as causal relations among variables of interest. There are multiple approaches to learn such models from data; however, most methods do not take available prior knowledge into consideration when learning a model, or consider certain types of prior knowledge that are not often available in practice. Prior knowledge often comes in the form of knowledge about causal or associative relations between pairs of variables. Such relations correspond to certain paths in a causal model. This type of knowledge naturally stems from domain experts, as well as observational and experimental data, among others. We develop theory and methods that use causal and associative prior knowledge when learning causal models. We approach the problem from two different perspectives. First, we consider the case of prior knowledge in the form of facts about the presence or absence of causal paths, and develop algorithms that incorporate it into existing causal models. Specifically, we consider the formalisms of Causal Bayesian Networks and Maximal Ancestral Graphs and their Markov equivalence classes: Partially Directed Acyclic Graphs and Partially Oriented Ancestral Graphs. We characterize the equivalence class of all graphs that belong in a Markov equivalence class and are consistent with a set of causal prior knowledge. We then introduce sound and complete procedures to incorporate causal knowledge in such models. In simulated experiments we show that our methods can make a large number of new inferences, even with a few prior knowledge facts. Subsequently, we consider knowledge in the form of prior beliefs (that is, having a degree of uncertainty) on certain causal or associative relations. We present a method that uses such beliefs to assign priors to all possible network structures, which can then be used by any searchand- score based method for learning graphical models. We also propose a novel search-operator to take further advantage of the prior beliefs. In contrast to previous approaches, our method can handle the case of dependent and possibly incoherent prior beliefs. In simulated proof-of-concept experiments we show that our method can indeed take advantage of prior knowledge, and that the proposed search-operator can significantly improve the quality of the learned models (EN)

text
Τύπος Εργασίας--Μεταπτυχιακές εργασίες ειδίκευσης

Parallel Progmamming Models
Συστήματα χρόνου εκτέλεσης
Εκτίμηση πόζας
Single-Chip-Cloud
Παράλληλα προγραμματιστικά μοντέλα
MapReduce
Runtime Systems

Πανεπιστήμιο Κρήτης (EL)
University of Crete (EN)

English

2013


Σχολή/Τμήμα--Σχολή Θετικών και Τεχνολογικών Επιστημών--Τμήμα Επιστήμης Υπολογιστών--Μεταπτυχιακές εργασίες ειδίκευσης



*Institutions are responsible for keeping their URLs functional (digital file, item page in repository site)