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Monte Carlo Methods Guojin Chen Christopher Cprek  Chris Rambicure
Monte Carlo Methods ,[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object]
Introduction (cont.) ,[object Object],[object Object]
Major Components ,[object Object],[object Object],[object Object],[object Object]
Major Components (cont.) ,[object Object],[object Object],[object Object]
Monte Carlo Example:  Estimating  
If you are a very poor dart player, it is easy to imagine throwing darts randomly at the above figure, and it should be apparent that of the total number of darts that hit within the square, the number of darts that hit the shaded part (circle quadrant) is proportional to the area of that part. In other words,
If you remember your geometry, it's easy to show that
(x, y) x = (random#) y = (random#) distance = sqrt (x^2 + y^2) if distance.from.origin (less.than.or.equal.to) 1.0  let hits = hits + 1.0
 
[object Object],[object Object],[object Object]
History of Monte Carlo Method ,[object Object],[object Object]
History of Monte Carlo method ,[object Object],[object Object],[object Object]
Student - William Sealy Gosset (13.6.1876 - 16.10.1937)  This birth-and-death process is suffering from labor pains; it will be the death of me yet. (Student Sayings)
In 1931 Kolmogorov showed the relationship between Markov stochastic processes and certain integro-differential equations.  A. N. Kolmogorov (12.4.1903-20.10.1987)
History (cont.) ,[object Object],[object Object],[object Object]
John von Neumann (28.12.1903-8.2.1957)
History (cont.) ,[object Object],[object Object],[object Object],[object Object]
 
Georges Louis Leclerc Comte de Buffon (07.09.1707.-16.04.1788.)
Buffon's original form was to drop a needle of length L at random on grid of parallel lines of spacing D. For L less than or equal D we obtain  P(needle intersects the grid) = 2 • L / PI • D.  If we drop the needle N times and count R intersections we obtain  P = R / N,  PI = 2 • L • N / R • D.
 
 
 
 
 
http://www.geocities.com/CollegePark/Quad/2435/history.html http://www-groups.dcs.st-and.ac.uk/~history/Mathematicians/Kolmogorov.html http://www-groups.dcs.st-and.ac.uk/~history/Mathematicians/Von_Neumann.html http://wwitch.unl.edu/zeng/joy/mclab/mcintro.html http://www.decisioneering.com/monte-carlo-simulation.html http://www.mste.uiuc.edu/reese/buffon/bufjava.html
Monte Carlo Methods: Application to PDE’s Chris Rambicure Guojin Chen Christopher Cprek
What I’ll Be Covering ,[object Object],[object Object],[object Object],[object Object]
Approximating PDEs with Monte Carlo Methods… ,[object Object],[object Object]
A Simple Integral ,[object Object],[object Object]
A Simple Integral (continued…) ,[object Object],[object Object],[object Object],[object Object]
A Simple Integral (continued…) ,[object Object],[object Object]
The Importance of Randomness ,[object Object],[object Object]
The Importance of Randomness (continued…) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Random Trials (continued…) ,[object Object],[object Object]
Example: Finite Difference Approximation to a Dirichlet problem inside a square ,[object Object],PDE BC
Dirichlet Problem (continued…) ,[object Object],[object Object],[object Object],[object Object]
Tour du Wino ,[object Object],[object Object],[object Object],[object Object]
How “Tour du Wino” is Played ,[object Object]
“ Tour du Wino” (continued…) ,[object Object],[object Object],[object Object]
“ Tour du Wino” (continued…) ,[object Object],[object Object]
Random Walks ,[object Object],[object Object]
“ Tour du Wino” Results Table
“ Tour du Wino” (continued…) ,[object Object],[object Object],[object Object]
“ Tour du Wino” (End of the Road) ,[object Object],[object Object],[object Object],[object Object]
Wrap-Up ,[object Object],[object Object],[object Object]
More References ,[object Object],[object Object],[object Object],[object Object],[object Object]
A Few Monte Carlo Applications Chris Rambicure Guojin Chen Christopher Cprek
What I’ll Be Covering ,[object Object],[object Object],[object Object]
Markov Chains ,[object Object],[object Object],[object Object]
A Good Markov Chain Example
 
Moving on….. ,[object Object]
Quantum Monte Carlo ,[object Object],[object Object],[object Object]
A Few Problems Using QMC ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Why Use QMC?
The Root of QMC: The Schrodinger Equation ,[object Object],[object Object]
Variational QMC (VMC) ,[object Object],[object Object],[object Object]
 
Difficulties With VMC ,[object Object],[object Object],[object Object],[object Object],[object Object]
The Limitation of VMC ,[object Object],[object Object],[object Object]
One Experiment Done Using QMC Total Energy Calculations -Can use Monte Carlo to calculate cohesive energies of different solids. -Table shows how much more accurate the QMC calculation can be.
The End ,[object Object],[object Object]
My Sources ,[object Object],[object Object],[object Object]

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Monte carlo

Notas do Editor

  1. R is the unit rectangle that contains the function f(x).
  2. If a dart player was performing this trial and hit a bullseye every time, this would be an example of this.
  3. The dart player was throwing randomly for quite a while, and then suddenly he starts throwing in a pattern of bullseye, then 11, bullseye, 11.
  4. I’ve read online about Monte Carlo simulations running for years. This kid is funny.
  5. In this example a reward is only given if the wino stumbles to the top of the square, or one of the three boundary points at the top.