18 research outputs found
Logical Hidden Markov Models
Logical hidden Markov models (LOHMMs) upgrade traditional hidden Markov
models to deal with sequences of structured symbols in the form of logical
atoms, rather than flat characters.
This note formally introduces LOHMMs and presents solutions to the three
central inference problems for LOHMMs: evaluation, most likely hidden state
sequence and parameter estimation. The resulting representation and algorithms
are experimentally evaluated on problems from the domain of bioinformatics
Parameter Learning of Logic Programs for Symbolic-Statistical Modeling
We propose a logical/mathematical framework for statistical parameter
learning of parameterized logic programs, i.e. definite clause programs
containing probabilistic facts with a parameterized distribution. It extends
the traditional least Herbrand model semantics in logic programming to
distribution semantics, possible world semantics with a probability
distribution which is unconditionally applicable to arbitrary logic programs
including ones for HMMs, PCFGs and Bayesian networks. We also propose a new EM
algorithm, the graphical EM algorithm, that runs for a class of parameterized
logic programs representing sequential decision processes where each decision
is exclusive and independent. It runs on a new data structure called support
graphs describing the logical relationship between observations and their
explanations, and learns parameters by computing inside and outside probability
generalized for logic programs. The complexity analysis shows that when
combined with OLDT search for all explanations for observations, the graphical
EM algorithm, despite its generality, has the same time complexity as existing
EM algorithms, i.e. the Baum-Welch algorithm for HMMs, the Inside-Outside
algorithm for PCFGs, and the one for singly connected Bayesian networks that
have been developed independently in each research field. Learning experiments
with PCFGs using two corpora of moderate size indicate that the graphical EM
algorithm can significantly outperform the Inside-Outside algorithm
Teoretické způsoby modelování uživatelského rozhodování
Táto práca sa zaoberá problematikou modelovania užívateľských preferencií. Obsahuje rozbor rozdielnych pohľadov na užívateľské preferencie. Práca obsahuje prehľad stávajúcich modelov užívateľských preferencií a porovnania medzi nimi. Podrobne rozoberá Fuzzy Logické Programovanie, Bayesove Logické Programovanie, Pravdepodobnostné Relačné Modely a Markovove Logické Siete. Pre jednotlivé modely sú navrhnuté transformácie do iných modelov a taktiež sú ukázané ich možnosti použitia v reálnom svete. V závere práce sú uvedené návrhy na rozšírenia jednotlivých modelov. Powered by TCPDF (www.tcpdf.org)In this thesis we address to the problematics of modelling user preferences. We discuss different views on user preferences as well as we give an overview of known models of user preferences and compare them. In more detail we introduce Fuzzy Logic Programming, Bayesian Logic Programming, Probabilistic Relational Models and Markov Logic Networks. For each model we propose transformations to other models and we show possible utilizations in real world. Finally we present our suggestions how to extend and improve these models. Powered by TCPDF (www.tcpdf.org)Katedra softwarového inženýrstvíDepartment of Software EngineeringMatematicko-fyzikální fakultaFaculty of Mathematics and Physic