It is a cost effective approach.

And two dollars profit per entrepreneur per day, which is like 700 dollars per year. And of course, it's very beneficial in a lot of ways.

We have increased our attendance, our pupil attendance, 3 00every single pupil month, as compared to the previous year, 3 04so created an environment, I think, of less problems in the classroom 3 09and in the school in terms of behavior, 3 11and a sense of students wanting to be at school.

This is being answered using Bayes rule, so this is the probability of being happy given that it rains times the probability that it rains over the probability of being happy.

So we divide by the total weight of all particles, and we get out those probabilities over here.

From that you can easily calculate the probability of measuring 'white', if a particle falls on a black square.

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.

So, we actually just learned some interesting lessons.

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

We could sum out or marginalize over the variable R to give us a table that just operates on T.

Now, this statement is not true.

For example, if we take the action go east into this state over here, with P probability we find ourselves over there.

We now apply Bayes rule.

But we don't know for sure that they're exclusive events.

It's our tendency to overestimate our likelihood of experiencing good events in our lives and underestimate our likelihood of experiencing bad events.

There's a 0.1 chance I might still be grumpy for some other reason.

To change this example even further.

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

I want to introduce the, the idea of probability here just, just in a very, very basic way.

My second test, after seeing a positive test, has now an increased probability of about 23 of coming in positive.

Reading improves the mind.

So, I think that in the Industrial Economy time Smith was more correct than Marx.

Very good. It increases both dexterity and direction.

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

Then we graph the binomial just like before.

What kind of odds would you have give you and me?

And the model that heads come is a probability of 1 3.

The probability of any random variable Y can be written as probability of Y given that some other random variable X assumes value i times probability of X equals i, sums over all possible outcomes i for the (inaudible) variable X.

This is just the theorem of total probability or forward propagation rule applied to this case over here, so nothing really new.

And then the utility of the game is the expected value, so we look at all the outcomes and the probability of each outcome.

That is not the case for song.

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.

If the true location is 1, which has 1 3 chance,

That's the only way you can improve from the midterm to the final.

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.

However, if I now divide, that is, I normalize those non normalized probabilities over here by this factor over here,

On the second state we know that the probability of R is 0.6 and therefore, the probability of sun is 0.4, and we compute the probability of rain on day 2 using total probability.

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.

And we know that our variance is essentially the probability of success times the probability of failure.

I want to spend a few minutes and go over the formal definition of localization.

The probability of observing X and Y depends on what state the hidden markov model is in.

For A, it's 0.1 for X and 0.9 for Y.

And the way they do this is through a combination of probability and data.

Do you adjust to killing animals?