We study again our die. You observe the following sequence 1, 2, 3, 2.

We find that along the main diagonal our uncertainty has shrunk substantially.

Obviously, it's 1 out of 4. It's 0.25.

And then we iterate.

This is very standard linear algebra.

In coin flipping, we flip n coins. In elections, we ask n randomly chosen people.

What this says is based on the outcome, we believe for the parameter µ.

Now I want take a second to convince ourselves this is indeed the maximum likelihood estimate of the mean and the variance.

So what do they want to know?

The sum over all E of the mu is mu equals sum over i, xi.

There's many different ways to implement this.

It's hours, people per hour.

We find for day 0 we had rain, and maximum likelihood would just say the probability for day 0 is 1.

Please use Laplacian smoothing for this with K equals 1.

In both cases you need to know the number of cluster centers, K.

Narrator And the answer is there's 8 different messages of which 3 are spam.

And we're going to want to square this as well.

Estimated time to strike intercept, two minutes.

In both of the transitions we go from rain to sun. There is no instance of rain to rain.

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Supposed suicide.

Compute for me the mean and the variance using the maximum likelihood estimator I just gave you.

I just want to tell you what you've learned, because you did amazingly well.

With the appropriate definition of value, we would get the following formula, which is 77.

We'll let R do the work.

And in the second box, give me an estimator that has one fake data point.

W 0, where 0 is your iteration number, and then you up with it iteratively.

In the 1 dimensional case, it is mu and sigma squared.

There are 4 possibilities 0 and 0, 1 and 0, 0 and 1, and 1 and 1.

Estimated time to firing range, 15 minutes.

And so these are going to be three values drawn from a Gaussian distribution with mean zero and variance or standard deviation equal to one.

Let me ask you again. Suppose you make 111 coin flips.

Stop estimating backup size

Simple extrapolation.

We talked about the Laplace estimator, which added k fake data points, 1 for each possible outcome, and that resulted in the slightly more complex out, as shown in this formula over here. and we identified cases where the Laplace estimator is much better than the maximum likelihood estimator, specifically when there isn't much data.

And this is probably the best estimate of housing prices I can find.

I will now teach you about estimators.

And of course the answers is 0, if p 0, the coin comes up tail all the time and as a result it won't vary, therefore there is no variance.

Dispersion

Variance

It turns out that c is also the actual cost, because at the goal the h component is 0, and so the path cost is the total cost as estimated by the function.

Let me give you an algorithm that defines value iteration.

We're almost at the correlation matrix.