https://syntheticsapien.com/ 2026-04-21T05:25:40-07:00 https://syntheticsapien.com/ https://syntheticsapien.com/lib/tpl/roundbox/images/favicon.ico text/html 2013-10-09T09:30:11-07:00 https://syntheticsapien.com/doku.php/goplayer:betanode?rev=1381336211&do=diff (add, sub, mul, div, cnd <http://editthis.info/logic/The_Laws_of_Classical_Logic> <http://en.wikipedia.org/wiki/List_of_axioms> <http://en.wikipedia.org/wiki/First-order_logic> The beta distribution is related to the gamma distribution. Let X be a random number drawn from Gamma(1,α) and Y from Gamma(1,β). Then Z=X/(X+Y) has distribution Beta(α,β). With this transformation, it should only take twice as much time as your gamma distribution test. text/html 2011-02-17T22:15:15-07:00 https://syntheticsapien.com/doku.php/goplayer:bnstatespace?rev=1298009715&do=diff Since a Bayesian network is a directed acyclical graph, it can be represented as a series of ordered nodes connected by directed arcs. The nodes are ordered in a hierarchy where a node cannot be the parent of another node higher in the hierarchy. In other words, the nodes are topologically ordered. text/html 2011-02-17T23:10:32-07:00 https://syntheticsapien.com/doku.php/goplayer:cptwrappers?rev=1298013032&do=diff class CPTWrapper(object): pass; class SimpleDiscreteCPTBN(CPTWrapper): def __init__(self, nodeOrder, map, stateSpace, jft): ''' nodeOrder: [node1, node2, node3, ...] map: {node1:[], node2:[node1], node3:[node1, node2], ...} stateSpace: {node1:['a', 'b', 'c'], node2:[1, 3, 5, 7], node3:['high', 'low'], ...} jft: {node1:[0, 2, 3, 1, 1, ...], node2:[3, 3, 1, 1, 2, ...], node3:[1, 1, 0, 0, 0, ...], ...} ''' self.__nodeMap = map; self.__orderedNodeList = nodeOrder; self.__state… text/html 2011-09-13T17:31:43-07:00 https://syntheticsapien.com/doku.php/goplayer:dbconnector?rev=1315960303&do=diff /* ============================================================================ Name : DBConnector.h Author : Stephen Cannon Version : 0.1 Copyright : Copyright 2011 Stephen Cannon Description : ============================================================================ * * This file is part of LikelihoodWeighting. * * LikelihoodWeighting is free software: you can redistribute it and/or modify * it under the terms of the GNU Lesser General Public License as publish… text/html 2023-02-24T23:05:23-07:00 https://syntheticsapien.com/doku.php/goplayer:dbrv?rev=1677308723&do=diff /* ============================================================================ Name : dbrv.h Author : Stephen Cannon Version : 0.1 Copyright : Copyright 2011 Stephen Cannon Description : ============================================================================ * * This file is part of LikelihoodWeighting. * * LikelihoodWeighting is free software: you can redistribute it and/or modify * it under the terms of the GNU Lesser General Public License as published by … text/html 2011-02-12T19:54:08-07:00 https://syntheticsapien.com/doku.php/goplayer:goplayer?rev=1297569248&do=diff So now that I’ve got my Master’s degree, I am looking to expand and test my knowledge in AI as well as see what I’m capable of. I think one of the best ways to achieve this goal is to enter one or more AI competitions. This way I can best test and hone my abilities against others more capable than me as well as get direct feedback regarding where my abilities lie in comparison to others. Basically, I feel any competition that forces me to improve my skills in AI are good for me at this stage … text/html 2023-02-24T23:05:23-07:00 https://syntheticsapien.com/doku.php/goplayer:likelihoodweighting?rev=1677308723&do=diff cdef extern from "dbrv.h": char dbrv__setup(DBRV *self, char *connectionString); char dbrv__logP(DBRV *self, char *nodeName, PyObject *parentValues, double *prob); char dbrv__randomSample(DBRV *self, char *nodeName, PyObject *parentValues, unsigned int *sample); char dbrv__tearDown(DBRV *self); cdef class NodeSample: cdef node cdef int stateIndex cdef list states cdef list isSet cdef list parents cdef class LikelihoodWeighting: cdef list __orderedNodes cdef size_t __numSample … text/html 2011-12-16T12:29:41-07:00 https://syntheticsapien.com/doku.php/goplayer:mcffnngmmdist?rev=1324067381&do=diff The Monte Carlo Multilayer Perceptron Gaussian Mixture Model Distribution (MCMPGMMDist). A Bayesian network node conditional probability distribution is represented by a series of Gaussian mixture models. A Gaussian mixture model is defined for every combination of states the parent nodes of the given node can take on. The parameters for each Gaussian mixture model is determined from a multilayer perceptron. As the mixture models for each set of parent states are not independent of each othe… text/html 2012-07-13T07:38:10-07:00 https://syntheticsapien.com/doku.php/goplayer:mcmpgmmdist?rev=1342190290&do=diff Explanation of the Monte Carlo Multilayer Perceptron Gaussian Mixture Model Distribution (MCMPGMMDist). A Bayesian network node conditional probability distribution is represented by a Gaussian mixture models. A Gaussian mixture model is defined for every combination of states the parent nodes of the given node can take on. The parameters for each Gaussian mixture model is determined from a multilayer perceptron. As the mixture models for each set of parent states are not independent of each… text/html 2011-02-13T01:06:59-07:00 https://syntheticsapien.com/doku.php/goplayer:rv?rev=1297588019&do=diff I wanted to use Python to define a random variable in a pythonic way. That way, I could intuitively work with random variables from within python and potentially even use some of Python's more powerful language features in conjunction with these random variables. text/html 2012-11-20T11:55:20-07:00 https://syntheticsapien.com/doku.php/goplayer:sidebar?rev=1353441320&do=diff * Main * Go Player * Python Representation of RVs * Python CPT Wrappers * Monte Carlo Multilayer Perceptron Gaussian Mixture Model Distribution * Beta Distribution as Fundamental Bayesian Network Node * BN State Space * DB Connector * DB RV * Likelihood Weighting