Stuff over here requires less memory, less computation, and guess what?

There are two things involved two parts to the explanation.

One explanation is that the person doesn't have the disease that's overwhelmingly likely, if you pick someone at random but the test gets it wrong, which is unlikely.

So, here's another quiz.

This expression is called the likelihood.

Obviously, we're observing 8 messages spam, spam, spam, and 5 times ham.

So it's a constraint problem. And there's a constraint that must be satisfied.

But in the correspondence step instead of making a hard correspondence we make a soft correspondence.

labeled anything, with any probability.

And the justification test is based on a memorization of a criterion that combines the negative log likelihood of your data itself and a penalty for each cluster.

Now, we probably think a priori that's unlikely.

You might remember all this work with Laplace smoothing, but for now we keep it simple, just maximum likelihood.

Now, we won't have to reject them like we do in rejection sampling, but they'll have a low probability associated with them.

The other explanation is that she was guilty.

Let's just plug in the numbers.

We end up walking around in this space of assignments of variables randomly.

Today we're frightened of the atom bomb.

You are a used up old maid who couldn't do nothing for nobody.

And the number we end up with that number which is a little bit less than one in 100 is to do with how likely one of those explanations is relative to the other.

Now let's look at the code you're going to have to write.

P of A given B where B is the evidence and A is the variable we care about is P of B given A times P of A over P of B.

Oh, Benson.

And here is my answer.

I wrote one. Let's see what it produces.

The initial probability for rain is 0 because we are just encountering 1 initial day and it's sunny.

looks as follows 3,9,9,3.

However I'd like to do this with Laplacian smoothing with k 1.

So, we're going to introduce a new method called likelihood weighting that generates samples so that we can keep every one.

The other explanation is that the person does have the disease that's unlikely but the test gets it right, which is likely.

There's many different ways to implement this.

All the other ones are examples of transitions from state to xt 1 to xt.

There is 5 data points.

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

Please use Laplacian smoothing for this with K equals 1.

And I'd like you to write a function called likelihood that takes the data and the distribution and returns the likelihood.

All right, but it might take some doing. You'll have to trust me.

Narrator And the answer is there's 8 different messages of which 3 are spam.

Linear regression? Do we compute confidence intervals?

That's why I asked you to meet me before I reported to the post.

In 3 cases, A is followed by A over here, over here, and over here.

And very certainly, the answer is yes.

One of them is that Sally Clark was innocent which is, a priori, overwhelmingly likely most mothers don't kill their children.

EM is a probabilistic generalization that also allows you to find clusters but also modifies the shapes of the clusters by modifying the covariance matrix.

Each data point is attracted to a cluster center in proportion to the posterior likelihood which we will define in a minute.

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