what is markov chian model? define it.?
344708
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.
648708
I know this! I did a problem like this a while ago! It's an exponential equation. It can be used to predict exponential increases or decreases, like bacteria or double/triple/quadrupled information.
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I assume that you mean for p, the only parameter.\n\nIt's k/n where k = number of successes in n trials.
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For n = 5? Seriously?\n\nIt is not clear what you are asking.\n\nSuppose that:\n\n1. Your model is of a sequence of independent Bernoulli trials each of which succeeds with probability p.\n\n2. Your null hypothesis H0 is p = 0.3\n\n3. Your alternative hypothesis H1 is p is not 0.3.\n\n4. You plan to observe 5 trials with the intent of accepting or rejecting the null hypothesis with 95% confidence.\n\nThen from p + q = 1 (i.e., q = 1 - p) and 1^5 = (p+q)^5 = p^5 + 5p^4q + 10p^3q^2 + 10p^2q^3 + 5pq^4 + q^5 you know that the probabilities are\n\n5 successes in 5 trials: p^5 = 0.3^5 = 0.00243\n4 succeses in 5 trials: 0.02835\n3 successes: 0.13230\n2 successes: 0.30870\n1 success: 0.36015\n0 successes: 0.16807\n\ni.e., 5 or 4 successes occur <= 5% of the time so you accept H0 on 3 or fewer successes and reject H0 on 4 or 5 successes.\n\nAnother interpretation of your question:\n\nSuppose your model is of independent identical Bernoulli trials each with unknown probability p of success and you would like to estimate p from observing the outcomes of 5 independent trials.  Given k out of 5 successes as the observed outcome for which p can we say that "k" or "k or fewer" or "k or more" successes out of 5 has probability <= 5% of being observed (that is three different questions by the way).  For each of k = 0, 1, ..., 5 you plot the probability of that outcome given p and exclude those p for which the plot drops to 5% or less.
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The best way to work this out would be to plot a speed time graph for the information.\n\nOr you could express it as a simultaneous equation.
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For found an answer watch here:\nhttp://en.wikipedia.org/wiki/Transition_matrix
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See p. 541 of Econometric Foundations by Mittelhammer, Judge and Miller.  Will be a bit tough to explain on Yahoo.  Actually, I don't like this book, but none of my other ones have this.
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n = N/(1+Ne(e))\n\nWhere:\nn = number of samples\nN = total population\ne = margin of error\n\nI could not find the squared key on my board so i just manipulated it, however it is the same.
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Bernoulli Effects\n\nSee how to demonstrate it here --> http://www.physics.umn.edu/outreach/pforce/Bernoulli.html
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This looks like one you should refer to your professor or advisor.  You might try searches at\n\nhttp://www.scirus.com/srsapp/\n\nhttp://www.scienceresearch.com/search/
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This sounds like it came out of a textbook. What is the name of the various propagation models?
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The mean passage time is the average between state changes in a system that exhibits Markov properties (ie a system in which the history of changes is not at all predictive, only the current state contains any predictive information)\n\nThese two links might help...
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i am not sure with who came up with the concept, but this might be usefull \n\nhttp://en.wikipedia.org/wiki/Probability_Density_Function
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Assume dc/dz is constant, n(t=T)=-DA(dc/dz) T\nBut in reality dc/dz often changes.  Normally dc/dz should decrease with time.  If it is a close system, it will reach the equilibrium state.
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check\nhttp://kmoddl.library.cornell.edu/model.php?m=557
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A Finite State Machine (FSM) is a model of behavior composed of states, transitions and actions. It is extremely useful for compiler construction. See source for details.
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The zig zag motion of freely suspended particles in a medium is called brownian motion.