what is the metropolis algorithm?
478057
It is a montecalro method based on the markov chain process, where in you calculate the change in energy of the system and then generate a random number and if the random number is less than exp((-Energy)/T) then you accept a move,else the move is rejected, and T is the parameter which controls the randomness of the system
834
Could be "Metropolitan Statistical Area".
769374
This sounds like it came out of a textbook. What is the name of the various propagation models?
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Prolog or Lisp. Machine learning\nMore detail please.\nI'm more for Java.\nBut most likely you are looking for the toolkit From MATLAB(tm) the first link.
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MCA (Mastering in Computer Applications)
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D = M/V\n\nDensity= Mass/Volume
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One cool way that I have seen it derived is based on Jayne's Formalism.\n\nAssume we sample process, and measure the mean and standard devation of the samples. There are many possible distributions that will produce those exact mean and standard deviation. \n\nJayne's Formalism chooses the distribution which maximizes the entropy of the distribuiton. This is viewed mathematically as the distribution that makes the minimum number of assumptions regarding the distribution.\n\nI always thought that was kinda cool.
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Hmm.... I can't seem to find one on the sites I've checked, but based on inference, I would guess it would be something like Argmt. or Agmt. These aren't official, mind you, but with a little hard work they could be ;) .
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here is a good  one\n\nhttp://www.snapfiles.com/reviews/AI_RoboForm/roboform.html
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http://en.wikipedia.org/wiki/Peephole_optimizer \n\nThis link tells you all about it :)
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www.kevin-wells.com/net/mdmcbx4.inf
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Markov chain is like a random walk. You start in a room and take one step in any direction. Then you take another in any direction. Keep doing this. Every step you take is totally independent of the last step.\n\nI've heard the term Markov chain model used in many contexts, so I can't give you any more information without clarification.
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mass initial = Mi\nmass final = Mf\n\nMass growth = Mf - Mi\nPercent growth = Mf/Mi * 100
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The Brown University pattern theory group is working with the belief that the world is complex, and to understand it, or a part of it, requires realistic representations of knowledge about it. We create such representations using a mathematical formalism, pattern theory, that is compositional in that the representations are built from simple primitives, combined into (often) complicated structures according to rules that can be deterministic or random. This is similar to the formation of molecules from atoms connected by various forms of bonds. \n\nPattern theory is transformational in that groups or semigroups of transformations operate on the primitives. These transformations express the invariances of the worlds we are looking at. \n\nPattern theory is variational in that it describes the variability of the phenomena observed in different applications in terms of probability measures that are used with a Bayesian interpretation. This leads to inferences that will be realized by computer algorithms. Our aim is to realize them through codes that can be executed on currently available hardware.
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http://en.wikipedia.org/wiki/MRAM
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Get some help here:\nhttp://en.wikipedia.org/wiki/Brownian_movement
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You are best off developing a system of equations and unknowns. For the most part at the beginning of the game, there will be more unknowns than equations, making a unique solution not possible at that time.\n\nThere are some techniques (Jayne's Formalism comes to mind) that use an entropy maximiztion solution to find a unique "good" solution in these cases.