Diffusion Processes and Related Topics in Biology by Luigi M. Ricciardi

By Luigi M. Ricciardi

These notes are according to a one-quarter path given on the division of Biophysics and Theoretical Biology of the college of Chicago in 1916. The path used to be directed to graduate scholars within the department of organic Sciences with pursuits in inhabitants biology and neurobiology. just a moderate acquaintance with likelihood and differential equations is needed of the reader. routines are interwoven with the textual content to motivate the reader to play a extra lively function and therefore facilitate his digestion of the fabric. One objective of those notes is to supply a heuristic strategy, utilizing as little arithmetic as attainable, to sure features of the idea of stochastic approaches which are being more and more hired in many of the inhabitants biol­ ogy and neurobiology literature. whereas the topic will be classical, the nov­ elty right here lies within the strategy and perspective, quite within the applica­ tions resembling the method of the neuronal firing challenge and its comparable dif­ fusion approximations. it's a excitement to thank Professors Richard C. Lewontin and Arnold J.F. Siegert for his or her curiosity and aid, and Mrs. Angell Pasley for her very good and cautious typing. I . PRELIMINARIES 1. Terminology and Examples contemplate an test laid out in: a) the experiment's results, ~, forming the gap S; b) definite subsets of S (called occasions) and via the possibilities of those events.

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32) f'(x' ' t'lx' 0' t' 0 ) changes Eq. 34) o Exercise. Prove it. ) Since Eq. 36) 59 thus showing that f is normal with mean m(t) = x o + p(t-t 0 ) and variance V(t) a 2 (t-t o ). Clearly, for p = 0 we recover the process earlier discussed. This process is again characterized by the stationary independent-increment property 2 and is known as Wiener process with drift p and infinitesimal variance a , originating at X(t o ) = xo. Fig. 36) at five successive times, for some p > O. -; t=2t t=t o t=3t o t=4t o o arto / I X Fig.

18) . the ( ) element of K-1 • where a ij 1S i,j 56 Exercise. 19) vhoro X • [~ 1=d T denoten the tr=,pone. 16). 21) as the covariance of X(t l ) and X(t 2 ), for an arbitrary process X(t). Clearly, K(t l ,t 2 ) = K(t 2 ,t l ), so that K = I IK(ti,tj)1 I is a symmetrix matrix. Furthermore, K(t,t) = Var[X(t)). In the case of the Wiener process, Eq. 22) since E[w(t l )) = E[w(t 2 )) = O. 23) for all t, and since as shown by Eq. 12), we can easily determine K(t l ,t 2 ). 22). 23). 24). 20) is the covariance matrix of W( t) • Exercise.

21) 51 as it is immediately seen by integrating Eq. 6) between Exercise. 4. 00 and x. What is the initial condition for F(x,tlxo ,t 0 )? The Wiener Process Let us now consider the simplest diffusion equation. 10) b(x) =0 and a(x) = 02/2,where This is obtained by 02 is a constant. We are thus considering a stationary diffusion equation(*) with zero drift and infinitesimal 2 variance equal to o. f. 2) These equations are not singular. We can, therefore, identify the diffusion interval with the entire real line.

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