related literature or studies of markov chain?
149013
Check out the links on Wikipedia:\n\nhttp://en.wikipedia.org/wiki/Markov_chain\n\nThe one on generating English language is interesting - it is based on one of Claude Shannon's early experiments in entropy.
545240
Sure.  Try these:\n\nhttp://www.people.fas.harvard.edu/~rtmoore/GMMC/heckman.pdf\nhttp://www.nottingham.ac.uk/economics/leverhulme/research_papers/05_44.pdf\n\nAnd if those don't help, try this:\n\nhttp://www.google.com/search?num=20&hl=en&lr=lang_en&sa=X&oi=spell&resnum=0&ct=result&cd=1&q=%22Heckman+Selection+Model%22&spell=1
663593
A unified family of goodness-of-fit tests is introduced and studied.\nThe new family of test statistics Sn(s) includes both the supremum\nversion of the Anderson-Darling statistic and the test statistic of\nBerk and Jones (1979) as special cases. The new family is based on\nphi-divergences somewhat analogously to the phi-divergence tests for\nmultinomial families introduced by Cressie and Read (1984), and is\nindexed by a real parameter s ∈ R: s = 2 gives the Anderson - Darling\ntest statistic, s = 1 gives the Berk-Jones test statistic, s = 1/2 gives\na new (Hellinger - distance type) statistic, s = 0 corresponds to the\n“reversed Berk-Jones” statistic studied by Jager and Wellner (2004),\nand s = −1 gives a “studentized” (or empirically weighted) version\nof the Anderson - Darling statistic. We also introduce corresponding\nintegral versions of the new statistics.\nWe show that the asymptotic null distribution theory of Jaeschke\n(1979) and Eicker (1979) for the Anderson-Darling statistic, and of\nBerk and Jones (1979) and Wellner and Koltchinskii (2003) for the\nBerk-Jones statistic, applies to the whole family of statistics Sn(s)\nwith s ∈ [−1, 2]. We also provide new finite-sample approximations\nto the null distributions and show how the new approximations can\nbe used to obtain accurate computation of quantiles.\nOn the side of power behavior, we show that for 0 < s < 1 and\nfixed alternatives the test statistics always converge almost surely to\ntheir corresponding natural parameter. For 1 < s < ∞ we provide\nnecessary and sufficient conditions on the alternative d.f. F for convergence\nto the corresponding natural parameter to hold, and show\nthat the “Poisson boundary” phenomena noted by Berk and Jones\nfor their statistic continues to hold for s ≥ 1 and s < 0 by identifying\nthe Poisson boundary distributions explicitly. We also briefly discuss\nfurther large deviation results and connections between the work of\nBerk and Jones (1979) and Groeneboom and Shorack (1981).\nWe extend the results of Donoho and Jin (2004) by showing that\nall our new tests for s ∈ [−1, 2] have the same “optimal detection\nboundary” for normal shift mixture alternatives as Tukey’s “highercriticism”\nstatistic and the Berk-Jones statistic.
575536
Here is a great article with sample problems.\n\nHope this helps!
951978
Look up convergence theory on www.colorado.edu/communication/met.  It will explain this theory in fairly simple terms.
440605
Binomial Distribution, \nPoisson Distribution, \nNormal Distribution\n\nhttp://www.futureaccountant.com/probability/
249369
Is the term you are looking for "probability"?\n\nI hope this helps.
805688
U can use either Kruskal's algorithm or prims algorithm. More information about these algorithm u can find on internet.\n\nPrims algorithm is based on adjacency matrix.
152796
try this site...\n\nhttp://www.futureaccountant.com/probability/
189594
There is a great book that may help you..\n\n"Data Structures and Algorithms in C++","Adam Drozdel"..\n\ncheck the website.\n:)
160196
Here is the book:  \nProtein Bioinformatics: An Algorithmic Appoarch To Sequence And Structure\nby Ingvar Eidhammer, Inge Jinassen, William R.Taylot\n\nFor More info look at this web page:\nhttp://en.wikipedia.org/wiki/Protein_structure
12763
There is a review called "ACM Transactions on Mathematical Software (TOMS)". Check out Volume 20, Issue 3 (September 1994)\nPages: 373 - 392   \n(ISSN:0098-3500)
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http://www.home.duq.edu/~weisberge/lect5.html
144611
You might find this paper useful\nhttp://arxiv.org/abs/math-ph/0106027
427277
Maybe you will find this article useful. It explains the concept of information entropy.
691515
algorithms, data structures, computer organization
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1- Arbib, M. A. (1969). Theories of Abstract Automata. Englewood\nCliffs, NJ: Prentice-Hall.\n2- Arbib, M. A. (1987). Brains, Machines and Mathematics. Second\nedition. New York: Springer.\n3- Arbib, M. A., Ed. (1995). The Handbook of Brain Theory and Neural\nNetworks. Cambridge, MA: MIT Press\n3- Chomsky, N. (1963). Formal properties of grammars. In R. D.\nLuce, R. R. Bush, and E. Galanter, Eds., Handbook of Mathematical\n4- Handbook of Mathematical Psychology, vol. 2. New York:\nWiley,  Minsky, M. L. (1967). Computation: Finite and Infinite Machines.\nEnglewood Cliffs, NJ: Prentice-Hall.\n5- Post, E. L. (1936). Finite combinatory processes—formulation I.\nJournal of Symbolic Logic 1: 103–105.\n6- Seidenberg, M. (1995). Linguistic morphology. In M. A. Arbib,\nEd., The Handbook of Brain Theory and Neural Networks.\nCambridge, MA: MIT Press.\n6- Shannon, C. E., and J. McCarthy, Eds. (1956). Automata Studies.\nPrinceton: Princeton University Press.\n7- Turing, A. M. (1936). On computable numbers. Proc. London\nMath. Soc. \n\nI can't work the format when I post my answer, but I reposted the answer with numbers so it can be easy for you to look at.