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        "author": "Kaustav Gopinathan",
        "author_email": "kaustav.gopinathan@gmail.com",
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        "description": "====\ncurv\n====\n\n**curv (Continuous Random Variables)** is a python package for creating \nand manipulating continuous random variables in a bayesian network. You \nmay find it useful for performing bayesian inference on \"broad\" \ndistributions with  high kurtosis or highly dimensional joint \ndistributions where discrete sampling would require excessive memory.\n\nTypical usage often looks like this::\n\n    #!/usr/bin/env python\n\n    import curv as cv\n\n    A = cv.Normal(1,2)\n    B = cv.Uniform(-5,4)\n    C = 4 + A - 2*B\n    D = C * A**2\n    cv.pdplot(D,-10,10)\n    cv.netplot()\n    A = cv.Normal(1,1)\n    cv.pdplot(D,-10,10)\n\n **Table of Contents** \n\n- [Installation & Dependencies](#)\n- [Usage](#)\n\t- [Creating a Bayesian Network](#)\n\t- [Performing Inference](#)\n\t- [Displaying Results](#)\n- [Under the Hood](#)\n\t- [Characteristic Functions](#)\n\t- [Joint Probability Characteristic Functions](#)\n\t- [Computing and Plotting Results](#)\n\t- [Bayesian Inference](#)\n\n\nInstallation & Dependencies\n===========================\n\nDependencies include:\n\n* Numpy\n\n* Scipy\n\n* Matplotlib\n\nUsage\n=====\nCurv stores its random variables as continuous functions as opposed to storing discrete probabilities in sampled bins. This data\nstructure reduces memory consumption (since distributions are not \nsampled and stored as individual points). \n\nAs a result, curv would be useful for handling large, highly dependent bayesian networks with high dimensional joint probability distribution functions without second-order approxiations (such as Chow-Liu trees).\n\nCurv would also be useful for distributions with high kurtosis, where discrete sampling over a large number of bins would be needed to capture rare events at the edges of the distribution.\n\nCreating a Bayesian Network\n---------------------------\n\nPerforming Inference\n--------------------\n\nDisplaying Results\n------------------\n\nUnder the Hood\n==============\n\nCharacteristic Functions\n------------------------\n\nJoint Probability Characteristic Functions\n------------------------------------------\n\nComputing and Plotting Results\n------------------------------\n\nBayesian Inference\n------------------\n\n",
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        "license": "MIT",
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        "name": "curv",
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        "summary": "Manipulate Bayesian Networks of Continuous Random Variables",
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