Can Continuous Approximation Improve Understanding of 1D Random Walks?

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SUMMARY

The discussion centers on the application of continuous approximation to the binomial probability distribution in 1D random walks, specifically when the total number of steps, denoted as ##N##, is large. The binomial distribution, represented as ##W(n_{1}) = \frac{N!}{n_{1}!(N-n_{1})!} p^{n_{1}} q^{N-n_{1}}##, exhibits a maximum at a specific value of ##n_{1}##, allowing for the approximation of ##W## as a continuous function of the variable ##n_{1}##. This approximation is valid in regions near the maximum, where the fractional change in ##W## is minimal, enabling the use of normal distribution techniques to calculate probabilities at non-integer values.

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Kashmir
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Reif,pg 14. ##n_1## is the number of steps to the right in a 1D random walk. ##N## are the total number of steps

"When ##N## is large, the binomial probability distribution ##W\left(n_{1}\right)##
##W\left(n_{1}\right)=\frac{N !}{n_{1} !\left(N-n_{1}\right) !} p^{n_{1}} q^{N-n_{1}}##
tends to exhibit a pronounced maximum at some value ##n_{1}=n_{1}##, and to decrease rapidly as one goes away from ##\tilde{n}_{1}##. Let us exploit this fact to find an approximate expression for ##W\left(n_{1}\right)## valid when ##N## is sufficiently large.
If ##N## is large and we consider regions near the maximum of ##W## where ##n_{1}## is also large, the fractional change in ##W## when ##n_{1}## changes by unity is relatively quite small, i.e.,
##\left|W\left(n_{1}+1\right)-W\left(n_{1}\right)\right| \ll W\left(n_{1}\right)##
Thus ##W## can, to good approximation, **be considered as a continuous function of the continuous variable ##n_{1}##**, although only integral values of ##n_{1}## are of physical relevance. The location ##n_{1}=\tilde{n}## of the maximum of ##W## is then approximately determined by the condition ##\frac{d W}{d n_{1}}=0 \quad##"

* I'm not able to understand why
##
\left|W\left(n_{1}+1\right)-W\left(n_{1}\right)\right| \ll W\left(n_{1}\right)
##means we can use a continuous approximation.

* How do we approximate a discrete function by a continuous one. Since ##W## has values defined only at integral values, what values do we assign to the continuous function between any two consecutive integers i.e what value does ##W(0.5)## have in the continuous approximation?
 
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I expect the author is referring to the normal approximation to the binomial distribution. You fit it by specifying the mean and variance of the normal distribution to equal the mean (np) and variance (npq) of the binomial. You can then calculate the PDF or CDF at any value rather than just integer values.
 

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