The document discusses risk modeling approaches, both old and new. The old approaches, such as single point estimates and manual what-if analyses, are time consuming and error prone for making multi-million dollar decisions. The new approach uses probability distributions derived from Monte Carlo simulations, which are necessary for defensible business decisions and managing risk. Monte Carlo simulation is a virtual experiment repeated many times using random samples within defined parameters to understand the behavior of a process.
Describes the math::exact module in Tcllib, which performs exact calculations over the computable reals. Numbers are represented as objects that contain programs to define the number by successive approximations.
Introduction to MATLAB Programming and Numerical Methods for Engineers 1st Ed...AmeryWalters
Full download : https://alibabadownload.com/product/introduction-to-matlab-programming-and-numerical-methods-for-engineers-1st-edition-siauw-solutions-manual/ Introduction to MATLAB Programming and Numerical Methods for Engineers 1st Edition Siauw Solutions Manual , Introduction to MATLAB Programming and Numerical Methods for Engineers,Siauw,1st Edition,Solutions Manual
Describes the math::exact module in Tcllib, which performs exact calculations over the computable reals. Numbers are represented as objects that contain programs to define the number by successive approximations.
Introduction to MATLAB Programming and Numerical Methods for Engineers 1st Ed...AmeryWalters
Full download : https://alibabadownload.com/product/introduction-to-matlab-programming-and-numerical-methods-for-engineers-1st-edition-siauw-solutions-manual/ Introduction to MATLAB Programming and Numerical Methods for Engineers 1st Edition Siauw Solutions Manual , Introduction to MATLAB Programming and Numerical Methods for Engineers,Siauw,1st Edition,Solutions Manual
Monte Carlo Simulations (UC Berkeley School of Information; July 11, 2019)Ivan Corneillet
My guest lecture on Monte Carlo simulations [or "how to be approximately right, now vs. precisely wrong, later (or never…)"] for the Managing Cyber Risk course of UC Berkeley School of Information's Cybersecurity Master.
This talk is going to cover some techniques of counting using a computer. Counting problems come up very often in areas of databases, networking, and elsewhere. Counting is so simple that by itself it isn’t even worth talking about, but there are some techniques that have truly impressive gains.
Notes accompanying these slides: http://www.slideshare.net/roshmat/counting-using-computer
Random Walks, Efficient Markets & Stock PricesNEO Empresarial
The famous financial theory of Efficient Markets is associated with the idea of a Random Walk. If the theory holds true, that makes prices unpredictable, and therefore it'd be impossible to consistently beat the market.
The seminar discusses the mathematical idea of a random walk, then moves on to understand what makes a market efficient.
Finally, we conduct a Monte Carlo Simulation on Wolfram Mathematica, to forecast the behaviour of Google's stock price one year from now.
Predictive Analytics for Everyone! Building CART Models using R - Chantal D....Chantal Larose
I had the pleasure to lead a wonderfully successful workshop March 8 2017, which focused on helping fellow faculty
across the disciplines learn how to utilize R programming and CART models in their research
Sudoku Solving with Computational Intelligenceharaldhiss
Computers can create convincing results – the cinemas are presenting the latest overwhelming 3D multimedia spectacles. The results and benefits aren’t restricted to the amusement and movies, modern methods for our machines bring further considerable improvements to reality. Programs and algorithms evolve in bioinformatics and public health. Computational intelligence can also be utilized in solving brainteasing tasks. This article is presenting a computational intelligence approach for solving Sudoku brainteasers.
A scanning strategy introduces Sudoku and solves the first example. The article presents an estimation of the complexity with stochastic methods. Sudoku-specific advanced strategies refine the scanning technique from the introduction. A software-architecture for efficiently implementing a rule-based solver is integrating the strategies and is using annotations that are updated with notifications. When no rule can be applied, a backtracking search is started. The parametrized solver offers policies for searching. A logging mechanism is presented supporting the search saving memory for the efficient implementation.
The author, Dr. Harald Hiss, has obtained the diploma in software-engineering in 2004 and he has graduated in databases in 2008 at the university of Freiburg supported by the interdisciplinary graduate programme logic and applications. This article is presenting a new Sudoku solver that performs best. The computer scientist is presenting Sudoku-specific strategies and a solver architecture for the fast and simple implementation using the latest technologies.
Monte Carlo Simulations (UC Berkeley School of Information; July 11, 2019)Ivan Corneillet
My guest lecture on Monte Carlo simulations [or "how to be approximately right, now vs. precisely wrong, later (or never…)"] for the Managing Cyber Risk course of UC Berkeley School of Information's Cybersecurity Master.
This talk is going to cover some techniques of counting using a computer. Counting problems come up very often in areas of databases, networking, and elsewhere. Counting is so simple that by itself it isn’t even worth talking about, but there are some techniques that have truly impressive gains.
Notes accompanying these slides: http://www.slideshare.net/roshmat/counting-using-computer
Random Walks, Efficient Markets & Stock PricesNEO Empresarial
The famous financial theory of Efficient Markets is associated with the idea of a Random Walk. If the theory holds true, that makes prices unpredictable, and therefore it'd be impossible to consistently beat the market.
The seminar discusses the mathematical idea of a random walk, then moves on to understand what makes a market efficient.
Finally, we conduct a Monte Carlo Simulation on Wolfram Mathematica, to forecast the behaviour of Google's stock price one year from now.
Predictive Analytics for Everyone! Building CART Models using R - Chantal D....Chantal Larose
I had the pleasure to lead a wonderfully successful workshop March 8 2017, which focused on helping fellow faculty
across the disciplines learn how to utilize R programming and CART models in their research
Sudoku Solving with Computational Intelligenceharaldhiss
Computers can create convincing results – the cinemas are presenting the latest overwhelming 3D multimedia spectacles. The results and benefits aren’t restricted to the amusement and movies, modern methods for our machines bring further considerable improvements to reality. Programs and algorithms evolve in bioinformatics and public health. Computational intelligence can also be utilized in solving brainteasing tasks. This article is presenting a computational intelligence approach for solving Sudoku brainteasers.
A scanning strategy introduces Sudoku and solves the first example. The article presents an estimation of the complexity with stochastic methods. Sudoku-specific advanced strategies refine the scanning technique from the introduction. A software-architecture for efficiently implementing a rule-based solver is integrating the strategies and is using annotations that are updated with notifications. When no rule can be applied, a backtracking search is started. The parametrized solver offers policies for searching. A logging mechanism is presented supporting the search saving memory for the efficient implementation.
The author, Dr. Harald Hiss, has obtained the diploma in software-engineering in 2004 and he has graduated in databases in 2008 at the university of Freiburg supported by the interdisciplinary graduate programme logic and applications. This article is presenting a new Sudoku solver that performs best. The computer scientist is presenting Sudoku-specific strategies and a solver architecture for the fast and simple implementation using the latest technologies.
1. Risk Modeling: The Old Way
Single Point Estimates
Do you make multi-million dollar decisions based on 1 number?
3-Point Estimates or Scenario Analyses
Do you make multi-million dollar decisions based on 3 numbers?
Manual What-if Analyses
Will you mandate that your analysts run a hundred scenarios for your
million dollar decision? How much will that cost you?!?
What if something changes? What if many things change,
and change at different times? Managing risk then becomes
cumbersome, time consuming, and error laden.
2. Probability Distributions
The direct result of Monte Carlo simulations,
which are absolutely necessary for making
defensible business decisions and for managing risk.
Risk Modeling: The New Way
Probability distributions furnish you with the full range of
possible outcomes, how likely those outcomes are to occur,
and identifies those items that impact your bottom line most
significantly and by how much.
3. What is Monte Carlo simulation?!?!
At its core, Monte Carlo simulation is a virtual experiment
– repeated hundreds, thousands, even millions of times –
all the while generating random samples, bound by a set
of parameters that you define, from each repetition of that
experiment.
It’s really not rocket science.
Those random samples are then collected, organized,
and analyzed to help you understand something about
the behavior of that process or system.
4. What is Monte Carlo simulation?!?!
Let’s use a common example to illustrate
. . . rolling a pair of dice . . .
What happens when we roll the dice 48 times . . . and collect the results?
Imagine we have only a vague
idea of how 2 dice behave when
rolled.
Let’s model it!
We define the parameters as:
“2 dice”
each with
“6 possible outcomes”
5. What is Monte Carlo simulation?!?!
We get a bunch of numbers . . .
Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total
1 4 5 3 6 9 4 3 7 6 6 12
2 2 4 2 4 6 2 2 4 3 4 7
3 3 6 1 1 2 3 1 4 1 1 2
4 1 5 6 2 8 1 6 7 6 2 8
1 6 7 5 3 8 6 5 11 5 3 8
6 5 11 2 5 7 5 4 9 2 5 7
Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total
6 1 7 3 4 7 3 3 6 3 4 7
4 2 6 6 2 8 6 1 7 6 2 8
1 3 4 2 3 5 2 6 8 2 3 5
2 4 6 4 1 5 4 1 5 4 1 5
3 5 8 5 5 10 5 2 7 5 6 11
5 6 11 1 5 6 1 6 7 1 5 6
6. What is Monte Carlo simulation?!?!
Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total
1 4 5 3 6 9 4 3 7 6 6 12
2 2 4 2 4 6 2 2 4 3 4 7
3 3 6 1 1 2 3 1 4 1 1 2
4 1 5 6 2 8 1 6 7 6 2 8
1 6 7 5 3 8 6 5 11 5 3 8
6 5 11 2 5 7 5 4 9 2 5 7
Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total
6 1 7 3 4 7 3 3 6 3 4 7
4 2 6 6 2 8 6 1 7 6 2 8
1 3 4 2 3 5 2 6 8 2 3 5
2 4 6 4 1 5 4 1 5 4 1 5
3 5 8 5 5 10 5 2 7 5 6 11
5 6 11 1 5 6 1 6 7 1 5 6
We then collect those numbers . . .
5 9 7 12
4 6 4 7
6 2 4 2
5 8 7 8
7 8 11 8
11 7 9 7
7 7 6 7
6 8 7 8
4 5 8 5
6 5 5 5
8 10 7 11
11 6 7 6
. . . and then condense the numbers into just the totals . . .
8. What is Monte Carlo simulation?!?!
The result is a chart that illustrates something about the
behavior of rolling dice.
7
7
7
7
7 8
5 6 7 8
5 6 7 8
5 6 7 8
4 5 6 7 8 11
4 5 6 7 8 11
2 4 5 6 7 8 9 11
2 4 5 6 7 8 9 10 11 12
12
F 10
r
e 8
q
u 6
n
c 4
y
2
0
2 3 4 5 6 7 8 9 10 11 12
9. What is Monte Carlo simulation?!?!
Let’s review . . .
Monte Carlo simulation is a virtual experiment that
repeats a process or project or situation a large number
of times, and generates a large number of random
samples bound by a specific set of parameters.
Those random samples are collected and then
organized and analyzed to help you understand the
behavior of a simple or complex system or process.