What's the worst case scenario?

What's my worst case scenario?

So, the first guiding principle is that we used what's often called worst case analysis. By worst case.

We have focused on the worst case scenario.

There's another tradition that thinks it's opposed to this but accepts the worst assumption.

logarithmic and as big, in the worst case, as linear.

Everybody in this room has heard the worst case scenarios.

An assumption and only an assumption.

And each hair has the worst case of split ends possible.

Simplifying the worst case, all we have is a while loop through every element in the list p, so this is clearly linear in the worst case.

So you might say, OK, worst case, these banks all go bankrupt.

Worst case, this is a zero, and then you get a 5.

In this case, the problem really comes down to the naive Bayes assumption is a weak one, and the Markov assumption would do much better.

Even in the worst case, she will not die of such an illness.

They can really re present and create their worst case scenario of suffering.

Randomizing might help avoid worst case behavior, but it's not the fastest way.

Go to the extreme in your image and see what will be the worst. In the worst case what is going to happen?

Worst

More data? Making the Markov assumption rather than naive Bayes assumption?

This is going to go through length of input list times in the worst case.

The worst case can be bad, but there's a way of fixing that as well.

So, as a result, worst case analysis is particularly appropriate for general purpose sub routines.

That's just an assumption.

It's all assumption driven.

In this video I'm going to work through that assumption, and see if that actually is the case.

The worst.

The worst.

What about the second assumption?

Now, this, so, sort of worst case guarantee popped out so naturally from our analysis of Merge

The conditional independence assumption of each pixel, given the class, is too strong an assumption in this case, but it's fun to talk about image recognition in the context of Naive Bayes regardless.

That's a big assumption, and we could test whether or not that assumption is valid.

Would it be helpful to make a Markov assumption rather than the naive Bayes assumption?

And what they particularly wanted to know, and asked many times was what is the worst case scenario.

It's the worst!

No, the worst.

It also meant, if the worst came to the worst,

And this is a huge assumption.

It's a reasonable assumption I think.

That's why this assumption is important.

In this case the worst and the best case are really the same, because no matter what, you traverse this for loop for the length of p.

The optional parameter workfactor controls how the compression phase behaves when presented with worst case, highly repetitive, input data.

And that it's accelerating even faster than what we thought was the worst case just a few years back.

By contrast, in worst case analysis, by definition you're making absolutely no assumptions about where the input comes from.

Namely, we're gonna pursue algorithms whose worst case running time grows slowly as a function of the input size.

For us that means making sure that people understand the worst case scenario for who will see the footage.