Before we get going with our discussion of inference methods, it is worth looking at the big picture for some perspective. So far, we have concentrated on ways to analyze probability models. This part of the picture. If our model has been selected in a careful way, then it should also be relevant to the …
Probability – L4.2 Overview of Some Application Domains Read More »
In this lecture, we start our systematic study of Bayesian inference. We will first talk a little bit about the big picture, about inference in general, the huge range of possible applications, and the different types of problems that one may encounter. For example, we have hypothesis testing problems in which we are trying to …
In this segment, we point out and discuss some important but also intuitive properties of conditional expectations. The first property is the one that is written up here. What is the intuitive meaning? If you condition on Y, then the value of Y is known, and so g of Y is also known. There’s no …
Probability – S13.1 Conditional Expectation Properties Read More »
We now continue the study of the sum of a random number of independent random variables. We already figured out what is the expected value of this sum, and we found a fairly simple answer. When it comes to the variance, however, it’s pretty hard to guess what the answer will be, and it turns …
Probability – L13.11 Variance of the Sum of a Random Number of Random Variables Read More »
We now study a model that involves the sum of independent random variables, but with a twist. It’s going to be the sum of a random number of independent random variables, as opposed to a fixed number. This is a model that shows up in a variety of applications, but it will also help us …
Probability – L13.10 Mean of the Sum of a Random Number of Random Variables Read More »
We will now go through another example to consolidate our intuition about the content of the law of iterated expectations and the law of the total variance. The example is as follows. We have a class, and that class consists of 30 students in total who are divided into sections– the first and the second …
We will now go through an example, which is essentially a drill, to consolidate our understanding of the conditional expectation and the conditional variance. Consider a random variable X, which is continuous and is described by a PDF of this form. Whenever we have a PDF that seems to consist of different pieces, it’s always …
We will now go through a derivation of the law of total variance. This particular derivation is not insightful. It will not really give you any intuition as to why the law of total variance is correct. On the other hand, it involves some interesting manipulations that will be useful to be able to follow, …
Probability – L13.7 Derivation of the Law of Total Variance Read More »
We have defined the conditional expectation of a random variable given another as an abstract object, which is itself a random variable. Let us now do something analogous with the notion of [the] conditional variance. Let us start with the definition of the variance, which is the following. We look at the deviation of the …
This is an example, or rather, a story, that’s supposed to give us some insight and some intuition about what the law of iterated expectations really means. Suppose that you work for a forecasting company. And suppose that you make forecasts by calculating expected values of the quantities that you want to forecast. And of …