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Approximate Bayesian Learning of Partition Directed Acyclic Graphs
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Approximativ bayesiansk inlärning av partitionerade acykliska grafer (Swedish)
Abstract [en]

Partition directed acyclic graphs (PDAGs) is a model whereby the conditional probability tables (CPTs) are partitioned into parts with equal probability. In this way, the number of parameters that need to be learned can be significantly reduced so that some problems become more computationally feasible. PDAGs have been shown to be connected to labeled DAGs (LDAGs) and the connection is summarized here. Furthermore, a clustering algorithm is compared to an exact algorithm for determining a PDAG. To evaluate the algorithm, we use it on simulated data where the expected result is known.

Abstract [sv]

Partitionerade riktade acykliska grafer (engelska: PDAGs) är en modell där tabeller över betingade sannolikheter partitioneras i delar med lika sannolikhet. Detta gör att antalet parametrar som ska bestämmas kan reduceras, vilket i sin tur gör problemet beräkningsmässigt enklare. Ett känt samband mellan PDAGs och betecknade riktade acykliska grafer (engelska: LDAGs) sammanfattas här. Sedan jämförs en klustringsalgoritm med en algoritm som exakt bestämmer en PDAG. Klustringsalgoritmens pålitlighet kollas genom att använda den på simulerad data där det förväntade resultatet är känt.

Place, publisher, year, edition, pages
TRITA-MAT-E, 2016:61
National Category
Mathematical Analysis
URN: urn:nbn:se:kth:diva-192853OAI: diva2:981303
Subject / course
Mathematical Statistics
Educational program
Master of Science - Applied and Computational Mathematics
Available from: 2016-09-29 Created: 2016-09-21 Last updated: 2016-09-29Bibliographically approved

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