Bayes’ rule: revising our prior beliefs

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so don't use a prior it's a language model will look at how do that in a minute.
The posterior is the thing we're after.
It's just the product off this likelihood on this prior.
So let's just think about that in these bays in terms before we even start the speech recognition we have in our mind a mental model of P.
O W.
You've all appear w in your minds.
Now you are making predictions about the next word.
I'm going to say you're making good predictions.
You're finding it easy to understand me if I just suddenly unexpectedly banana, say a word that you would expect You're not going to find that as easy to process.
Okay, so your language model did not have that he was there, but with a very, very low probability that then became harder for you to process.
So we're sitting there waiting with belief about what W will be with Beijing.
It's Noah value the distribution, but it's a non uniform distribution.
Something's likely something less like we're sitting there waiting with some belief.
Along comes some evidence in the form of a speech signal on the speech signal.
Revise is those beliefs, our beliefs after we hear what I've said different before, what I've said, hopefully unless I'm totally predictable.
Okay, which case my signals, totally uninformative.
Prior just takes over.
Hopefully there is some information in the speak stream and that revise your beliefs about BMW.
Thanks.
So this posterior is the prior POW, but revised now that we've seen.
So it's a distribution overall, lots of worse sequences, but it's being revised and it might be revised to the point where we're absolutely certain it was this word and we totally said it was none of the other words or it might be just a bit more spiky, hopefully got lesson for me.