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SushilShrestha/PMF-PDF.md

Last active January 31, 2019 01:21
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Probability Mass Functions / Probability Density functions
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PMF-PDF.md

PDF is defined by a Probability Distribution curve. PDF can be thought of like, given a sample data points, the PDF squashes the points to get a model (curve or distribution curve) that can be used to define the total samples. We can select a pdf train it on our dataset. find the pdf parameters and then use the model to predict future events. So PDF is a function p(x) that returns probability of x and uses parameters we obtained from the population.

We have freedom to choose what to use for p(x). There are multiple density functions you can choose from.

  • Bernoulli's Distribution
  • Poission's Distribution
  • Normal Distribution
  • Bionomial Distribution
  • Negative Binomial Distribution

Which distributions to use ? Well, it depends on what kind of datasets we have.

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