-
Notifications
You must be signed in to change notification settings - Fork 420
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Hidden Markov Models #44
Comments
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
HMM can be viewed as dynamic Bayesian networks
Discrete Markov Process
Transition prob of states
Use Markov to describe the transition
s means state, t - 1, each one we have n value, if no limited, the probability is hard to specify
Given the state at time
t - 1
, we don't care aboutt-2
anymore because it is a first-order Markov chainThe transition from state i to state j.
If I know today's weather, I can guess tomorrow's weather based on the understanding of past weather.
An example
For example, Today is rainy, tomorrow will be cloudy, the probability is 0.3, based on the matrix.
Given the weather on day 1 being sunny, what's the prob that the following 7 days are sun-sun-rain-rain-sun-cloudy-sun?
Convert to more formal format:
{S3,S3,S3,S1,S1,S3,S2,S3}
=> Find P(O|Model)
Extension to an HMM
Now:
We also want to observe if we have the same weather for the next few days.
Specifying an HMM
Given HMM
The text was updated successfully, but these errors were encountered: