January 2026

In this segment, we’re going to go over a few theoretical properties of the estimation error in least mean squares estimation. Recall that our least mean squares estimator is the conditional expectation of the unknown random variable, given our observations. Let us define the error, which is the difference between the estimator and the random […]

Our discussion of least mean squares estimation so far was based on the case where we have a single unknown random variable and a single observation. And we’re interested in a point estimate of this single unknown random variable. What happens if we have multiple observations or parameters? For example, suppose that instead of a

We now continue with our example and turn to the performance evaluation question. As you recall, we have a Theta that has a certain prior distribution. We’re given a model for the observations. We came up with the joint distribution for X and Theta, which was uniform on this particular shape, and we found that

Let us now go through an example. Suppose that we have an unknown random variable Theta that has a uniform distribution between 4 and 10. We observe some other random variable X that’s related to Theta according to the following model. This is the conditional distribution of X given Theta. For any given value of

Least mean squares estimation is remarkable because it has such a simple answer. The way to come up with estimates, if what you care about is to keep the mean squared error small, the way to come up with estimates is to just report the conditional expectation, which is going to be a number, once

After our warm-up, we can now come to the real problem. We have, again, a random variable Theta with a known prior distribution. And we’re interested in a point estimate. What will be different this time, however, is that we now have an observation. And we also have a model of that observation as a

In this segment, we introduce the subject of least mean squares estimation. But as a warm-up, we will start with a very simple special case. Suppose that we have some random variable that we wish to estimate and that we have the probability distribution of this random variable– a probability mass function if it’s discrete

We have seen so far two ways of estimating an unknown parameter. We can use the maximum a posteriori probability estimate, or we can use the conditional expectation. That is, the mean of the posterior distribution. These were, in some sense, arbitrary choices. How about imposing a performance criterion, and then finding an estimate which

Let us now come back to the trajectory estimation problem that we introduced earlier. We have an object that moves vertically. At any given time t, the height at which the object is found is equal to this expression. It corresponds to the following– the object starts at time 0, at some initial height Theta0,

The trajectory estimation problem that we considered gives you a first glimpse into a large field that deals with linear normal models. In this segment, I will just give you a preview of what happens in that field, although, we will not attempt to prove or justify anything. What happens in this field is that