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.

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

So what do they want to know?

There's many different ways to implement this.

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.

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

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

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Supposed suicide.

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.

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.

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

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

Stop estimating backup size

Simple extrapolation.

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

I will now teach you about estimators.

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.

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Control estimates their descent at 100 meters an hour.

Estimated time of death? Coroner puts time of death somewhere between 10 and 1 last night.

let me explain it to you.

In this sample, they might derive an estimate on how people are likely to vote.

Estimated writing speed

You presume correctly.

...because we hope there are survivors

It's a really simple formula. It's what's called the empirical count.

When I run it, it runs the routine particle filter with those motions and those measurements as an input.

It's hours, people per hour.

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Here's the idea for gradient descent.

If we look at the numbers over here, we get 0.5833 and 0.5986.

All we're going to test is whether your particle filter gives us a good estimate when we choose randomly the initial position of the robots, measurements, motions, and so on.

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