So this is a timely problem.

U.N., which came out on Friday.

Take a quick look at this graph.

The third mystery is this

Suppose the same dynamics apply from D2 to D3, so just replace D3 over here with D2s over there.

So what do they want to know?

If you compare how Obama did against how John Kerry had done four years earlier

Here's what the temperature has been on our Earth.

At this point, our robot has localized itself.

If there's no cell at all, there's a wall like over here, we assume with 80 chance, we just bounce back to the same cell, but with 10 chance, we instead go left.

So this unit is a tough one.

This next chart looks at more recent data.

That is, if I execute the east action of this state over here with probability 1 item over here, if I assume the north action over here, probability 1,

The next technique for efficient inference is to maximize independence of variables.

Here is our sequence. There's a couple of sunny days 5 in total a rainy day, 3 sunny days, 2 rainy days.

Corporate bankruptcies continued at a high level last month.

Then we get, by virtue of our expansion of the state at time T, the probability of transitioning from rain to rain is 0.6, the probability of having it rain is X again, the probability of transitioning from sun to rain is 0.2, and the probability of having sun before is 1 minus X, so we get X equals 0.4X plus 0.2.

In the second question we apply total probability.

So, here's another quiz.

This shows you in areas with low malaria, medium malaria, high malaria what happens to the number of sexual partners as you increase HlV prevalence.

And we can denote that by the probability that we get a Result state, S prime given that we start in state, S, and apply action, A.

Now, it may seem like we made a backwards step.

Here is the profile of employees of

States are A and B.

So we have 0.5 over here, 0.5 over here, 1 over here, and 0 over here for the transition probabilities.

This one, this one, and one out here, and I put a little check marks underneath.

Here we're given a marchov chain between A and B, with the transition of A to itself is 0.9 with 0.1 chances transitions to B.

Right. As with the printing press, if it's really a revolution, it doesn't take us from Point A to Point B.

So here's a multiple choice question for particle filters.

A flows into B in another 3 cases over here, over here, and over here again, 3 7.

And what you can see is that over the last 25 years,

For those of you who saw An Inconvenient Truth, the most important slide in the Gore lecture is the last one, which shows here's where greenhouse gases are going if we don't do anything, here's where they could go.

So 55 say A and 45 say B.

You might have heard about Bayes Rule before.

For example, for the prior probability, we found that 3 8 messages are spam.

If you could bring up the lights higher, because I'm just going to use a piece of paper here.

We asked them to estimate their likelihood of experiencing different terrible events in their lives.

Let's again use the rainy and sunny example just to keep it simple.

We looked at the prior probability of those grid cells at time t 1.

This is a degenerate case.

Twice it goes to sunny over here and over here, twice it goes to rainy over here and over here, so therefore the probability for either transition is 0.5.

Here we had one probability to estimate.

If you understood this, you understand probability, and you understand localization.

Got a reference?

Let's assume we do U 1, which means with 0.8 chance in each action we transition 1 to the right.