Bayes’ rule: P(O)

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So that's our hmm that we can have a guess about how we're gonna computer and we'll probably later.
This thing on the bottom's a bit weird.
It's just the probability of the speech was just being given.
Now how would we compute the probability of just some secrets of our CC vectors? Well, the only way we could do that it's just to see all possible sequences, ever CT vectors, and then look this one up in that distribution and say This is a really likely sequence of the mercy seat.
There's really unlikely sequence of species that would be a really, really hard thing to compute content out.
We don't need a computer.
That's okay, and we'll say precisely why Just in a minute, Okay, this is a thing that we want, and it's the thing that we want to use for the classification task.
So we were trying to maximise the value of this by varying W to make this term's biggest possible and then announced that W as the winner because this's found after getting the observations.
It's called the posterior austerity just means after this post here is a popular for this.
Okay, so the terms on the right inside.
Then Pierrot given W.
Is the likelihood that a German computes it.
W is the fryer is going to computed by something called the language model MP.
Evo is the prior on the observations.
It's a bit of a weird thing.
Think about what happens when we run the speech.
Recognise er, we get given a no fixed oh secrets of Assisi vectors.
Go look at them in the files with H List.
Print them out for the purpose of that one run of the recognise er between receiving it on printing the worst sequence out.
That's a constant.
Let's give him it doesn't really matter what probabilities maybe is probably his 0.3 May's probably ability is 0.1.
It never changes.
It's a constant, so it means we don't need a computer.
So this thing here is some unknown constant value.
We don't know what it is, but it's constant.
It's fixed, so just imagine it taking some value, I don't know, no idea what he was going to take.
So during recognition, we're just going to very w going to sweep through the recovery of W's trying to maximise this term here Gonna try this? W try this other dude.
You try the others, we're going to search amongst all the values that W could take for the W that makes this term's biggest possible.
And as we sweeping the W, it's just isolated words.
We just try one each in turn that sequence of words with final the sequences as we're doing that Oh, never changes.
So this value on the right never changes.
So we could just turn this equal sign into this other sign.
This just means proportional to, you know, there was a equals, a constant times, this thing just a scaled version of it.
And the constant is Pierrot.
So what really going to do is this thing Pierre was gone.
Good news, because it turns out to be really, really hard to calculate some sorts of models out there.
Fancy descriptive models need to compute turns like that's a big problem.
Arrangements.
We do not need to compute it.
So we don't have the thing.
The hmm computes thing.
This language model that's gonna come later computes we're ready to go.
We're just going to now compute each of those two terms They were going to very w across all the different values W could take for one of those values.
This value here will be the biggest, the maximum, and that's the W will announces the recognition results.
Okay, happy.
So decide.
They're sweeping across all of our use of W the space of all possible W's in our isolated.
Did you recognise her? Is very small and finite.
It's just 10 possible values we just couldn't list.
Try them in turn.
But more generally, it might be a sequence of words, and so we have to search amongst all possible sequences, so there might be some clever things we need to do there.
We'll come on to that later.