Exploring Continuous Approximation of 1D Random Walk Steps (Reif, pg 14)

In summary: This can be useful for calculations and approximations, but it's important to remember that the binomial distribution is still discrete and the normal approximation is just an approximation. In summary, when considering a large number of steps in a 1D random walk, the binomial probability distribution exhibits a maximum at some value ##n_{1}=\tilde{n}## and decreases rapidly as one moves away from this value. This allows for an approximate expression for ##W\left(n_{1}\right)## to be found when ##N## is large. The author suggests using a continuous approximation for ##W\left(n_{1}\right)##, although only integral values of ##n_{1}## are physically relevant. This can be
  • #1
Kashmir
468
74
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?
 
Science news on Phys.org
  • #2
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.
 

FAQ: Exploring Continuous Approximation of 1D Random Walk Steps (Reif, pg 14)

What is a 1D random walk?

A 1D random walk is a mathematical model that describes the movement of a single particle or object in one dimension, where the direction of each step is chosen randomly.

How is continuous approximation used in 1D random walks?

Continuous approximation is used in 1D random walks to simplify the model by replacing discrete steps with a continuous function, making it easier to analyze and calculate probabilities.

What is the significance of Reif's research on continuous approximation of 1D random walk steps?

Reif's research provides a deeper understanding of the behavior of 1D random walks and how they can be approximated using continuous functions. This can have applications in various fields such as physics, biology, and economics.

Can continuous approximation accurately represent a 1D random walk?

Yes, continuous approximation can accurately represent a 1D random walk as long as the steps are small and the number of steps is large. As the number of steps increases, the approximation becomes more accurate.

What are some limitations of using continuous approximation in 1D random walks?

One limitation is that it assumes the steps are small and do not deviate significantly from the mean. It also does not account for any external factors that may affect the direction of the steps, such as obstacles or varying environments.

Back
Top