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Wyszukujesz frazę "Markov graph" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Solving Markov decision processes by d-graph algorithms
Autorzy:
Kátai, Z.
Powiązania:
https://bibliotekanauki.pl/articles/205688.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
Markov decision processes
dynamic programming
graph representation
graph algorithms
optimization problems
Opis:
Markov decision processes (MDPs) provide a mathematical model for sequential decisionmaking (sMDP/dMDP: stochastic/ deterministic MDP). We introduce the concept of generalized dMDP (g-dMDP) where each action may result in more than one next (parallel or clone) state. The common tools to represent dMDPs are digraphs, but these are inadequate for sMDPs and g-dMDPs. We introduce d-graphs as general tools to represent all the above mentioned processes (stationary versions). We also present a combined d-graph algorithm that implements dynamic programming strategies to find optimal policies for the finite/infinite horizon versions of these Markov processes. (The preliminary version of this paper was presented at the Conference MACRo 2011.)
Źródło:
Control and Cybernetics; 2012, 41, 3; 577-593
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Learning causal theories with non-reversible MCMC methods
Autorzy:
Krajewska, Antonina
Powiązania:
https://bibliotekanauki.pl/articles/2183467.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
Bayesian inference
causal systems
directed acyclic graph
MCMC
non-reversible Markov processes
search and score methods
Opis:
Causal laws are defined in terms of concepts and the causal relations between them. Following Kemp et al. (2010), we investigate the performance of the hierarchical Bayesian model, in which causal systems are represented by directed acyclic graphs (DAGs) with nodes divided into distinct categories. This paper presents two non-reversible search and score algorithms (Q1 and Q2) and their application to the causal learning system. The algorithms run through the pairs of class-assignment vectors and graph structures and choose the one which maximizes the probability of given observations. The model discovers latent classes in relational data and the number of these classes and predicts relations between objects belonging to them. We evaluate its performance on prediction tasks from the behavioural experiment about human cognition. Within the discussed approach, we solve a simplified prediction problem when object classification is known in advance. Finally, we describe the experimental procedure allowing in-depth analysis of the efficiency and scalability of both search and score algorithms.
Źródło:
Control and Cybernetics; 2021, 50, 3; 323--361
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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