Abstract
Mathematical modeling is the art of translating problems from an application area into tractable mathematical formulations whose theoretical and numerical analysis are already studied thoroughly by statisticians. Getting familiar with frequently used mathematical models can help you apply a theoretical statistical training to an statistical application in the real world. Mathematical Models can provides mathematical insight, answers and guidance when we are building machine learning classifiers.
You will be able to learn about different probability distributions, properties and their applications in the real world. It's a multi-part series in which I am planning to cover the following:
- Bernoulli Distribution
- Uniform Distribution
- Binomial Distribution
- Poisson Distribution
- Geometric Distribution
- Negative Binomial Distribution
- Hypergeometric Distribution
- Beta Distribution
- Exponential Distribution
- Gamma Distribution
- Normal Distribution
- Chi Square Distribution