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Some Related Sentences

** and Markov
** Georgi Markov, Bulgarian dissident ( b. 1929 )
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or 15. D9 * 16. G11 * 17. G12 * 18. E5 * 19. i11 * 20. i8 21. H9 * 22. G9 * 23. D4 * 24. C4 * 25. J8 * 26. J6 ** 27. F7 trying to draw 28. J7 black must not remove his i8 / J6 link yet 29. L5 * now black can play at i5, remove the i8 / J6 link, and add the chain of three links H8 / J7 / i5 / K4.
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Markov and chain
* In analysis of Markov chain Monte Carlo data, autocorrelation must be taken into account for correct error determination.
In most cases, the computation is intractable, but good approximations can be obtained using Markov chain Monte Carlo methods.
In the 1980s, there was a dramatic growth in research and applications of Bayesian methods, mostly attributed to the discovery of Markov chain Monte Carlo methods, which removed many of the computational problems, and an increasing interest in nonstandard, complex applications.
Taken together, the two define a Markov chain ( MC ).
A famous paper by mathematician and magician Persi Diaconis and mathematician Dave Bayer on the number of shuffles needed to randomize a deck concluded that the deck did not start to become random until five good riffle shuffles, and was truly random after seven, in the precise sense of variation distance described in Markov chain mixing time ; of course, you would need more shuffles if your shuffling technique is poor.
* Markov chain Monte Carlo
Optimized Markov chain algorithms which use local searching heuristic sub-algorithms can find a route extremely close to the optimal route for 700 to 800 cities.
The key Discordian practice known as " Operation Mindfuck " is exemplified in the character of Markoff Chaney ( a play on the mathematical random process called Markov chain ).
Any version of Snakes and Ladders can be represented exactly as an absorbing Markov chain, since from any square the odds of moving to any other square are fixed and independent of any previous game history.
It is based on a Markov chain with two states G ( for good or gap ) and B ( for bad or burst ).
This was used then as a counter-example to the idea that the human speech engine was based upon statistical models, such as a Markov chain, or simple statistics of words following others.
In the simple case of discrete time, a stochastic process amounts to a sequence of random variables known as a time series ( for example, see Markov chain ).
In the 1980s, there was a dramatic growth in research and applications of Bayesian methods, mostly attributed to the discovery of Markov chain Monte Carlo methods, which removed many of the computational problems, and an increasing interest in nonstandard, complex applications.
* Mathematical notes on Bayesian statistics and Markov chain Monte Carlo
Queueing systems use a particular form of state equations known as a Markov chain that models the system in each state.
* Markov chain geostatistics
In statistics and in statistical physics, the Metropolis – Hastings algorithm is a Markov chain Monte Carlo ( MCMC ) method for obtaining a sequence of random samples from a probability distribution for which direct sampling is difficult.
The general idea of the algorithm is to generate a series of samples that are linked in a Markov chain ( where each sample is correlated only with the directly preceding sample ).
Too large or too small a jumping size will lead to a slow-mixing Markov chain, i. e. a highly correlated set of samples, so that a very large number of samples will be needed to get a reasonable estimate of any desired property of the distribution.
* Although the Markov chain eventually converges to the desired distribution, the initial samples may follow a very different distribution, especially if the starting point is in a region of low density.
The Markov chain is started from an arbitrary initial value and the algorithm is run for many iterations until this initial state is " forgotten ".
The result of three Markov chain s running on the 3D Rosenbrock function using the Metropolis-Hastings algorithm.
Category: Markov chain Monte Carlo
For example, a collection of walkers in a Markov chain Monte Carlo iteration is called an ensemble in some literature.
A simple two-state Markov chain

1.770 seconds.