Modelos formales en bioinformática

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One-dimensional nearest-neighbour random walks

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A random walk is a random process consisting of a sequence of discrete steps of fixed length. In the case of one-dimensional nearest-neighbour random walks, the reachable states are integer (or natural) numbers and at each step, the process jumps to the nearest-neighbour to the right with probability p or to the nearest-neighbour to the left with probability 1 - p.

A random walk with state space equal to Z (integer numbers) can be transient or null recurrent depending on p. If p=1/2 then it is null recurrent and in other cases it is transient. Look at the book by S. Ross (Example 4.3d).

A random walk with reflectant barrier at 0 (i.e., state space equal to non negative integers) can be transient, null recurrent or positive recurrent. Look here (local copy).

Written by Javier Campos

octubre 22nd, 2012 at 8:00 am

Posted in Material

Some more slides on SSA

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The SECOND part of the slides about “Stochastic Simulation of Biochemical Reactions” is available here.

Written by Jorge Júlvez

diciembre 2nd, 2010 at 9:27 am

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Stochastic Simulation Algorithms

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The first part of the slides about “Stochastic Simulation of Biochemical Reactions” is available here.

Written by Jorge Júlvez

noviembre 25th, 2010 at 10:47 am

Posted in Material

Birth-Death processes

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The slides for birth-death processes are available here.

Written by Jorge Júlvez

noviembre 10th, 2010 at 2:37 pm

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On the octopus Paul (and 3)

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octopus3

Written by Javier Campos

noviembre 9th, 2010 at 12:41 pm

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On the octopus Paul (2)

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octopus121

1 Technique not explained in the classroom, see for instance these notes.

Written by Javier Campos

noviembre 5th, 2010 at 4:41 pm

Posted in Material

On the octopus Paul (1)

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octopus_1When I commented in the classroom the two first questions of the exercise of the octopus Paul (modelling its mind states with a DTMC), I misinterpreted the words “it is in state 1 just before…” as “it has been always in the state 1 before…”. Obviously, if we interpret the sentence correctly (“it is in state 1 at step n…”), we need to compute the transient probability distribution (after n steps), using the expression

nstepI will publish here a solution for the exercise in the next days.

Written by Javier Campos

noviembre 5th, 2010 at 9:47 am

Posted in Material