| [Temporary Example] Buffon's 
              Needle by Michael 
              J. Hurben  
              
                Source 
              Code: buff.java 
                  |  |  
             
                "Buffon's Needle refers to a simple Monte Carlo method 
                  for the estimation of the value of pi, 3.14159265... The idea 
                  is very simple. Suppose you have a tabletop with a number of 
                  parallel lines drawn on it, which are equally spaced (say the 
                  spacing is 1 inch, for example). Suppose you also have a pin 
                  or needle, which is also an inch long. If you drop the needle 
                  on the table, you will find that one of two things happens: 
                  (1) The needle crosses or touches one of the lines, or (2) the 
                  needle crosses no lines. The idea now is to keep dropping this 
                  needle over and over on the table, and to record the statistics. 
                  Namely, we want to keep track of both the total number of times 
                  that the needle is randomly dropped on the table (call this 
                  N), and the number of times that it crosses a line (call this 
                  C). If you keep dropping the needle, eventually you will find 
                  that the number 2N/C approaches the value of pi! " continue 
                  to M. 
                  Hurbens Buffon's Needle page.  Each throw of a needle is an "event" in this simulation. 
              By event we mean that a single and complete process is simulated 
              and that it involves some degree of randomness and variation each 
              time. As the number of such events increases, the statistical error 
              on the calculation of a property of interest gradually becomes smaller 
              and smaller.  Another example of an event simulation would be a particle collision 
              program in which only after many collisions are simulated does the 
              calculation of, say, a reaction cross-section, become statistically 
              significant. Most recent update: Nov. 9, 2005 |