Then the value of the state for the action go up would be obtained as follows.

Let's use the Laplacian smoother with K 1 to calculate the few interesting probabilities

Probability

Phil, the odds against

And these are mutually exclusive events, you can't have both of them

If I pick a random T value, if I take a random T statistic

There's going to be a 10 percent chance you get a pretty good item.

Now let's have something a little bit more interesting.

We now apply Bayes rule.

What are the odds?

For example, in the last row we have a r and a t. r is 0.9.

least maybe it doesn't make intuitive sense just yet, but I showed you that the probability of a and b is equal to the probability of a given b times the probability of b.

In this case, there's only one such variable, Cloudy.

You wrote an algorithm that implements what's called Markov localization.

Better odds tonight?

Thrun As usual, we can resolve this using Bayes' rule.

Question 1 In the first question, I'm going to ask you some very basic probability questions.

You might say OK, that's the probably of getting exactly 1 times the probability of getting 2 out of 3 plus the probability of getting 3 out of 3.

Probability of success

Probability of failure

And we get this, the probability of b given a is equal to this, probability of a given b.

Time the probability of two sided coin.

And it makes much more sense to talk about the probability or random variable equaling a value, or the probability that it is less than or greater than something or the probability that is has some property

The probability of getting one condition, of an object being a member of set A, or, or a member of set B, is equal to the probability that it is a member of set A, plus the probability that it is a member of set B, minus the probability that it is a member of both. minus the probability that it is a member of both.

The smallness of that probability is what we mean by extremity.

In the second question we apply total probability.

Those values are discrete.

And this is the probability of four out of six heads, given an unfair coin.

So let's look at this, let's look at a population where the probability of success we'll define success as 1 as having a probability of p, and the probability of failure, the probability of failure is 1 minus p.

So is this a discrete or a continuous random variable?

How did we get this?

That happens in less than 2 of our missions.

So, we actually just learned some interesting lessons.

It starts out and there's a chance node.

To change this example even further.

And here is my answer. You can really read off the formula that I just gave you.

What's the probability of the joint X, Y?

Frack the odds.

Equally possible,

And here's my code, this implements Bayes rule.

Then I just normalize.

If I keep expanding this, I get the following solution.

Divided by the probability of in general, the probability of getting 5 out of 5 heads.

That is not the case for song.

You learned about probability of an event, such as the outcome of a coin flip.