Summary

A random variable is so called because it can take more than one state. The probability mass function specifies the probability for it to land on a particular state. Therefore, whenever you think of a random variable you should immediately think of its PMF (or histogram if you prefer). The PMF is a complete characterization of the distribution of a random variable. Once you have the PMF, you can derive the CDF, expectation, moments, variance, and so on. Note, however, that two random variables with the same PMF are not necessarily the same random variable, as we saw in Section sec: ch3 pmf with \(X\) and \(Y = -X\): they have identical PMFs but map the outcomes differently.

When your boss hands a dataset to you, which random variable (which model) should you use? This is a very practical and deep question. We highlight three steps for you to consider:

• (i) Model selection: Which random variable is the best fit for our problem? Sometimes we know by physics that, for example, photon arrivals or internet traffic follow a Poisson random variable. However, not all datasets can be easily described by simple models. The models we have learned in this chapter are called the parametric models because they are characterized by one or two parameters. Some datasets require nonparametric models, e.g., natural images, because they are just too complex. Some data scientists refer to deep neural networks as parametric models because the network weights are essentially the parameters. Some do not because when the number of parameters is on the order of millions, sometimes even more than the number of training samples, it seems more reasonable to call these models nonparametric. However, putting this debate aside, shortlisting a few candidate models based on prior knowledge is essential. Even if you use deep neural networks, selecting between convolutional structures versus long short-term memory models is still a legitimate task that requires an understanding of your problem.
• (ii) Parameter estimation: Suppose that you now have a candidate model; the next task is to estimate the model parameter using the available training data. For example, for Poisson we need to determine \(\lambda\), and for binomial we need to determine \((n,p)\). The estimation problem is an inverse problem. Often we need to use the PMF to construct certain optimization problems. By solving the optimization problem we will find the best parameter (for that particular candidate model). Modern machine learning is doing significantly better now than in the old days because optimization methods have advanced greatly.
• (iii) Validation. When each candidate model has been optimized to best fit the data, we still need to select the best model. This is done by running various tests. For example, we can construct a validation set and check which model gives us the best performance (such as classification rate or regression error). However, a model with the best validation score is not necessarily the best model. Your goal should be to seek a good model and not the best model because determining the best requires access to the testing data, which we do not have. Everything being equal, the common wisdom is to go with a simpler model because it is generally less susceptible to overfitting.