تحسين النتائح
ترتيب حسب
من
الى
الصفحة 1
الصفحة 1
Bayesian Networks and Decision Graphs
Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis. The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams.It contians two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems.
Markov Decision Processes with Their Applications
Markov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. MDPs can be used to model and solve dynamic decision-making problems that are multi-period and occur in stochastic circumstances. There are three basic branches in MDPs: discrete-time MDPs, continuous-time MDPs and semi-Markov decision processes. Starting from these three branches, many generalized MDPs models have been applied to various practical problems. These models include partially observable MDPs, adaptive MDPs, MDPs in stochastic environments, and MDPs with multiple objectives, constraints or imprecise parameters.
Alternatives Considered But Not Disclosed : The Ambiguous Role of PowerPoint in Cross-Project Learning
This study investigates the role of PowerPoint in organizational communication, particularly in terms of a functional dilemma between its application for documentation as opposed to presentation purposes. The theoretical part of the analysis combines insights from both organizational communication studies (J. R. Taylor et al.) and social systems theory (N. Luhmann et al.). The empirical analysis shows that PowerPoint documents created for cross-project learning purposes contribute to an invisibilization rather than a visibilization of decision processes and their contingency.
