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Monte Carlo Simulations
by Gio
Other
0
Basic Principle: Monte Carlo Method
• The Monte Carlo Method is the approach (methodology) of using
randomness to describe problems that may have a deterministic
solution
• The Law of Large Numbers (LLN) states that with an increase in the
number of measurements the expected value grows to equal the
average value
• A Pseudo-Random Number Generator is an algorithm for generating
numbers that appear reasonably random in sequence
1
Law of Large Numbers
2
Pseudo Random Number Generator
• Approximate randomness
• Probability distributions [0, 1]
3
Monte Carlo Steps
• Step 1: Define the probability space and the points within that space; use a large number of points
to define the space
• Step 2: Define the conditions of the problem that constrains the space
• Step 3: Discriminate between the points that reside within the constraints of the problem and those
that do not
• Step 4: Use the points that reside within the constraints to define the space of the solution
• Important: The greater the number of points, the greater the accuracy of the simulation
4
Colab Exercises
• Conditional Probability
• Basic Geometry
• Calculus
5
Conditional Probability
• Which combination of numbers, on average, give a larger value; three
numbers between 1-4 or two number between 1-6? Always double
the lowest number in each combination.
6
Basic Geometry
• Determine the area of basic geometric shapes without using the area
formulas for those shapes
• Relative areas
7
Calculus
• Determine the area under the curve without integrating
• Random sampling
8
Thank you!
9

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CI-Monte-Carlo.ppt

  • 2. Basic Principle: Monte Carlo Method • The Monte Carlo Method is the approach (methodology) of using randomness to describe problems that may have a deterministic solution • The Law of Large Numbers (LLN) states that with an increase in the number of measurements the expected value grows to equal the average value • A Pseudo-Random Number Generator is an algorithm for generating numbers that appear reasonably random in sequence 1
  • 3. Law of Large Numbers 2
  • 4. Pseudo Random Number Generator • Approximate randomness • Probability distributions [0, 1] 3
  • 5. Monte Carlo Steps • Step 1: Define the probability space and the points within that space; use a large number of points to define the space • Step 2: Define the conditions of the problem that constrains the space • Step 3: Discriminate between the points that reside within the constraints of the problem and those that do not • Step 4: Use the points that reside within the constraints to define the space of the solution • Important: The greater the number of points, the greater the accuracy of the simulation 4
  • 6. Colab Exercises • Conditional Probability • Basic Geometry • Calculus 5
  • 7. Conditional Probability • Which combination of numbers, on average, give a larger value; three numbers between 1-4 or two number between 1-6? Always double the lowest number in each combination. 6
  • 8. Basic Geometry • Determine the area of basic geometric shapes without using the area formulas for those shapes • Relative areas 7
  • 9. Calculus • Determine the area under the curve without integrating • Random sampling 8

Editor's Notes

  1. https://colab.research.google.com/drive/1D7iUKkEnrlmknKKrVC4VYUpYZx54ex9l?usp=sharing https://colab.research.google.com/drive/144Exi-nGqhckvsFfUMzkts5S0UUwbZwp?usp=sharing