Buffon's needle
In mathematics, Buffon's needle problem is a question first posed in the 18th century by Georges-Louis Leclerc, Comte de Buffon:
- Suppose we have a floor made of parallel strips of wood, each the same width, and we drop a needle onto the floor. What is the probability that the needle will lie across a line between two strips?
Using integral geometry, the problem can be solved to get a Monte Carlo method to approximate π.
Solution
The problem in more mathematical terms is: Given a needle of length <math>l</math> dropped on a plane ruled with parallel lines t units apart, what is the probability that the needle will cross a line?
Let x be the distance from the center of the needle to the closest line, let θ be the acute angle between the needle and the lines, and let <math>t\ge l</math>.
The probability density function of x between 0 and t /2 is
- <math> \frac{2}{t}\,dx. </math>
The probability density function of θ between 0 and π/2 is
- <math> \frac{2}{\pi}\,d\theta. </math>
The two random variables, x and θ, are independent, so the joint probability density function is the product
- <math> \frac{4}{t\pi}\,dx\,d\theta. </math>
The needle crosses a line if
- <math>x \le \frac{l}{2}\sin\theta.</math>
Integrating the joint probability density function gives the probability that the needle will cross a line:
- <math>\int_{\theta=0}^{\frac{\pi}{2}} \int_{x=0}^{(l/2)\sin\theta} \frac{4}{t\pi}\,dx\,d\theta = \frac{2 l}{t\pi}.</math>
For n needles dropped with h of the needles crossing lines, the probability is
- <math>\frac{h}{n} = \frac{2 l}{t\pi},</math>
which can be solved for π to get
- <math>\pi = \frac{2{l}n}{th}.</math>
Now suppose <math>t < l</math>. In this case, integrating the joint probability density function, we obtain:
- <math>\int_{\theta=0}^{\frac{\pi}{2}} \int_{x=0}^{m(\theta)} \frac{4}{t\pi}\,dx\,d\theta ,</math>
where <math>m(\theta) </math> is the minimum between <math>(l/2)\sin\theta</math> and <math>t/2 </math>.
Thus, performing the above integration, we see that, when <math>t < l</math>, the probability that the needle will cross a line is
- <math>\frac{h}{n} = \frac{2 l}{t\pi} - \frac{2}{t\pi}\left\{\sqrt{l^2 - t^2} + t\sin^{-1}\left(\frac{t}{l}\right)\right\}+1.</math>
Lazzarini's estimate
Mario Lazzarini, an Italian mathematician, performed the Buffon's needle experiment in 1901. Tossing a needle 3408 times, he attained the well-known estimate 355/113 for π, which is a very accurate value, differing from π by no more than 3×10−7. This is an impressive result, but is something of a cheat.
Lazzarini chose needles whose length was 5/6 of the width of the strips of wood. In this case, the probability that the needles will cross the lines is 5/3π. Thus if one were to drop n needles and get x crossings, one would estimate π as
- π ≈ 5/3 · n/x
π is very nearly 355/113; in fact, there is no better rational approximation with fewer than 5 digits in the numerator and denominator. So if one had n and x such that:
- 355/113 = 5/3 · n/x
or equivalently,
- x = 113n/213
one would derive an unexpectedly accurate approximation to π, simply because the fraction 355/113 happens to be so close to the correct value. But this is easily arranged. To do this, one should pick n as a multiple of 213, because then 113n/213 is an integer; one then drops n needles, and hopes for exactly x = 113n/213 successes.
If one drops 213 needles and happens to get 113 successes, then one can triumphantly report an estimate of π accurate to six decimal places. If not, one can just do 213 more trials and hope for a total of 226 successes; if not, just repeat as necessary. Lazzarini performed 3408 = 213 · 16 trials, making it seem likely that this is the strategy he used to obtain his "estimate".
See also
External links and references
- Buffon's Needle at cut-the-knot
- Math Surprises: Buffon's Noodle at cut-the-knot
- MSTE: Buffon's Needle
- Buffon's Needle Java Applet
- Estimating PI Visualization (Flash)
- Ramaley, J. F. (Oct 1969). "Buffon's Noodle Problem". The American Mathematical Monthly. 76 (8): 916–918.
- Mathai, A. M. (1999). An Introduction to Geometrical Probability. Gordon & Breach. Unknown parameter
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