Narrator So, I'm going to ask you a tricky question and maybe you can calculate this.

We now sample 3 new particles, with replacement.

In this case, let's choose Sprinkler, and we look at the rows in the table for which Cloudy, the parent, is positive, and we see we should sample with probability 10 to s and 90 a s.

Those together have a probability of 0.6 to be drawn, which contrasts to the 0.4 for P3.

It ignores measurements, because the measurement sets the weighting factor in this resampling process, but with only 1 particle the same particle will be resampled no matter what.

And assuming we got a probability of 0.9 came out in favor of the w, that would be the end of the sample.

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

Probability

Phil, the odds against

We get a 0.8 probability for Rain being positive, and a 0.2 probability for Rain being negative.

What are the odds?

And the way you derive this is by asking the question, what's the probability that particle 'A' is never sampled?

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

Probability of success

Probability of failure

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

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

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.

We now apply Bayes rule.

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?

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.

Frack the odds.

Equally possible,

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

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

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.

Confidence

Adaptive

Always sample

Maximum sample

Sample radius

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

So I read in the complete works of Shakespeare into a small computer program, and then built n gram models and sampled from that model.

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.

The motions don't move at all, move right, move down, move down, and move right again.

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

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

Including Richard Kimble.

Let's think about another one.

Let's do another example.

So we're not using this definition anymore.