Hands-On Machine Learning for Algorithmic Trading

Maximum likelihood estimation

MLE is an important general method to estimate the parameters of a statistical model. It relies on the likelihood function that computes how likely it is to observe the sample of output values for a given set of both input data as a function of the model parameters. The likelihood differs from probabilities in that it is not normalized to range from 0 to 1.

We can set up the likelihood function for the linear regression example by assuming a distribution for the error term, such as the standard normal distribution:

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This allows us to compute the conditional probability of observing a given output given the corresponding ...