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SamIam is a comprehensive tool for modeling and reasoning with Bayesian networks, developed in Java by the Automated Reasoning Group of Professor Adnan Darwiche at UCLA. Samiam includes two main components: a graphical user interface and a reasoning engine.
Get Started with SamIam. Welcome to the SamIam program! If you are a new user of SamIam, and you have access to a Windows computer, the first thing we recommend you to do is to view the introductory video tutorial (WMV / MP4) - it gives a basic introduction to the program, including: the differences between Edit Mode and Query Mode, how to ...
The invocation script provided with SamIam (samiam.bat on Windows, runsamiam on Solaris) helps you do that. By default, the script gives the JVM 512 Megabytes. If your system has more memory, we recommend you increase the maximum memory limit in the script to the amount of physical RAM present on your system.
SamIam is a comprehensive tool for modeling and reasoning with Bayesian networks, developed in Java by the Automated Reasoning Group of Professor Adnan Darwiche at UCLA. Samiam includes two main components: a graphical user interface and a reasoning engine.
SamIam's Sensitivity Analysis is a powerful, user-friendly tool for analyzing the complex dependencies between variables in a Bayesian network and providing guidance to the belief engineer as he tweaks network parameter values.
Most Probable Explanation (MPE) To calculate the most probable explanation for an open belief network, open the MPE tool by selecting its menu item from the "Query" menu or by clicking the "MPE" button on the tool bar. MPE is available only using the shenoy-shafer algorithm.
An in-depth discussion of what features of Genie files SamIam does and does not support, including: diagnosis node types, submodels, noisy or semantics, and some information on trouble-shooting SamIam's Genie file support.
SamIam’s MAP tool provides an interface with which to define and calculate an answer to a Maximum a Posteriori query. Invoke the tool by selecting its menu item from the "Tools" menu or by clicking the "MAP" button on the tool bar.
SamIam's EM Learning tool allows the belief engineer to learn the conditional probabilities in a network based on data, using the Expectation Maximization (EM) algorithm . The data must be in the form of a Hugin "case file," each line of which represents one instantiation of the variables in the network.
SamIam supports opening files in six popular formats for defining Bayesian networks: the Hugin .net format (v5.7 and v6.*), the Genie .dsl and .xdsl formats, the Interchange .dsc format used by the Microsoft Bayesian Network Toolkit, the Netica .dne format and the Ergo .erg format.
