Training – the problem is that the state sequence is hidden

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state secret is hidden, so we know we're Beijing's.
We're happy now, hopefully with this idea that state sequences around them variable and it could take all possible body was all at the same time.
And they just all existing little parallel universes on.
We don't need to just pick one.
They just all exist with very probabilities.
Remember the coin tossing experiment? How many coins we toss our coin.
We don't look at the result.
If we're free contest, we're just going to guess a fixed value heads we right half the time Wrong.
Half the time for a Beijing will maintain uncertainty.
Well, say, Well, I believe it's half heads and half tails.
Maintain this uncertain distribution.
It was a different philosophical way of thinking about it.
Same with a hidden Markov model has hidden Markov model inside a box.
We can't know his parameters and speech pops out.
Random observations of speech pop out.
We think what's happening inside the box.
Hmm.
Well, it's simple, tasty, using all possible state sequences and something them together to generate a probability of this speech.
There isn't a single answer to the States, so we don't know it.
So in this a bit of a Catch 22.
We have no model, it's got no parameters.
And therefore we can't work out which calcium generated which observation.
So we need to start somewhere.
Weaken state.
We don't approve it.
It's just being proved over and over again.
There is no single equation that, given some speech on Givens, are like blank prototype.
Hmm just writes down in one step, the mean of state three equals some equation involving the data.
There's no such equation.
All we can do is make the model a bit closer to the data and then in direct.