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Probability of a discrete variable obtaining value $x$. We cut some corners in ourĭefinitions here, but these definitions are functional for most of ourĪ probability mass function (PMF), $f(x)$, describes the Review of probability distributions, PMFs, PDFs, and CDFs ¶īefore we begin talking about distributions, let's remind ourselves
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Importantly, if your data and model match the story of a distribution, you know that this is the distribution to choose for your likelihood.
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These distributions all have stories associated with them. In most practical cases, though, your model is composed of standard probability distributions. In some cases, you need to derive the likelihood (or even numerically compute it when it cannot be written in closed form). Specifying your model amounts to choosing a probability distributions that describe the process of data generation. Take together, these models comprise a generative model, which describes how the data were generated. Specification of the likelihood and prior.
#CALTECH JUPYTER NOTEBOOK TUTORIAL CODE#
Before you work through the notebook, define your notebook_url in the code cell below. It will be something like localhost:8888. You can find this at the top of your browser. When you launch your notebook, take note of the URL.
#CALTECH JUPYTER NOTEBOOK TUTORIAL DOWNLOAD#
You should therefore download and run the notebook. This tutorial also features interactive plotting, which needs a running Jupyter notebook to utilize. Lewandowski-Kurowicka-Joe (LKJ) distribution.Because this tutorial consists mainly of a list of probability distributions and their stories and properties, it is useful to have a little index of them up front.