Explore >> Select a destination
|
You are here 
|
parametricity.com | ||
| | | | |
gist.github.com
|
|
| | | | | Generate text from an input using a simple Markov chain generator - markov.py | |
| | | | |
kevinkle.in
|
|
| | | | | I used Markov chains to generate images. | |
| | | | |
sookocheff.com
|
|
| | | | | A common method of reducing the complexity of n-gram modeling is using the Markov Property. The Markov Property states that the probability of future states depends only on the present state, not on the sequence of events that preceded it. This concept can be elegantly implemented using a Markov Chain storing the probabilities of transitioning to a next state. | |
| | | | |
djalil.chafai.net
|
|
| | | This post is devoted to few convex and compact sets of matrices that I like. The set \( {\mathcal{C}_n} \) of correlation matrices. A real \( {n\times n} \) matrix \( {C} \) is a correlation matrix when \( {C} \) is symmetric, semidefinite positive, with unit diagonal. This means that \[ C_{ii}=1, \quad C_{ji}=C_{ji},\quad \left\geq0 \] for every \(... | ||
