Estimates of population parameters based on samples are not exact: there is always some error involved. In principle, one can estimate a population parameter with any estimator, but some will be better than others. There is one particular case which was always very confusing to me (because of the multiple alternatives) and that is the estimation of the variance of a Normal population from a sample. In this article I describe the properties and tradeoffs among the different alternatives and discuss how important these differences are.

Maximum likelihood estimates of a distribution Maximum likelihood estimation (MLE) is a method to estimate the parameters of a random population given a sample. I described what this population means and its relationship to the sample in a previous post.
Before we can look into MLE, we first need to understand the difference between probability and probability density for continuous variables. Probability density can be seen as a measure of relative probability, that is, values located in areas with higher probability will get have higher probability density.

tl;dr: Parametric bootstrap methods can be used to test hypothesis and calculate p values while assuming any particular population distribution we may want. Non-parametric bootstrapping methods can be used to test hypotheses and calculate p values without having to assume any particular population as long as the sample can be assumed to be representative of the population and one can transform the data adequately to take into account the null hypothesis.

tl;dr: P-values are tail probabilities calculated from the sampling distribution of a sample-based statistic. This sampling distribution will depend on the size of the sample, the statistic being calculated and assumptions about the random population from which the data could have been sampled. For a few cases, analytical p-values are available and, for the rest of cases, approximations based on Monte Carlo simulation can be computed by generating the sampling distribution from the population.

tl;dr: Given any model that maps inputs to outputs, Dual Numbers can be used to calculate the exact derivative of any output with respect to any input without requiring the user to calculate these derivatives. An implementation of a Dual Number system only requires to define a data type or class with methods for every function and operator used inside the model.
What is Automatic Differentiation?
Automatic Differentiation (AD) is a series of methods to calculate the derivative of outputs of a model with respect to its inputs.

2018 - present · Powered by the Academic theme for Hugo.