**Markov Chains, Processes, Fields, Networks (Mar)**

Markov chain / (FLSU:D)Additive Markov chain Bayesian network / Bay Birth-death process / (U:D) CIR process / scl Chapman–Kolmogorov equation / (F:DC) Cheeger bound / (L:D) Conductance Contact process Continuous-time Markov process / (U:D) Detailed balance / (F:D) Examples of Markov chains / (FL:D) Feller process / (U:G) Fokker–Planck equation / scl anl Foster's theorem / (L:D) Gauss–Markov process / Gau Geometric Brownian motion / scl Hammersley–Clifford theorem / (F:C) Harris chain / (L:DC) Hidden Markov model / (F:D) Hidden Markov random field Hunt process / (U:R) Kalman filter / (F:C) Kolmogorov backward equation / scl Kolmogorov’s criterion / (F:D) Kolmogorov’s generalized criterion / (U:D) |
Krylov–Bogolyubov theorem / anl Lumpability Markov additive process Markov blanket / Bay Markov chain mixing time / (L:D) Markov decision process Markov information source Markov kernel Markov logic network Markov network Markov process / (U:D) Markov property / (F:D) Markov random field Master equation / phs (U:D) Milstein method / scl Moran process Ornstein–Uhlenbeck process / Gau scl Partially observable Markov decision process Product-form solution / spr Quantum Markov chain / phs Semi-Markov process Stochastic matrix / anl Telegraph process / (U:B) Variable-order Markov model Wiener process / Gau scl |

Read more about this topic: Catalog Of Articles In Probability Theory, Core Probability: Selected Topics

### Famous quotes containing the word networks:

“The great *networks* are there to prove that ideas can be canned like spaghetti. If everything ends up by tasting like everything else, is that not the evidence that it has been properly cooked?”

—Frederic Raphael (b. 1931)

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