Maximum Likelihood Estimation For Regression
Maximum likelihood estimation or otherwise noted as MLE is a popular mechanism which is used to estimate the model parameters of a regression model. Other than regression, it is very often used in statics to estimate the parameters of various distribution models.
A section wise summary of the artical is as follows. Although post is written with assumption of reader being started from begining, feel free to jump to any section at your desire.
-Normal / Gaussian distribution
-Binomial distribution
-What is MLE
-How to Calculate Likelihood
-How to use MLE for linear regression
Normal / Gaussian distribution
In probability the normal or gaussian distribution is a very famous continous probability distribution. It basically depends on two factors — the mean and the standard deviation. The mean of the distribution determines the location of the center of the graph and the standard deviation determines the height and width of the graph. So notation of normal distribution becomes:
PDF of Normal distribution is:
Note: from a pdf function, we can get the probability related to a data point x. So by substituting values for x, the relevant…
