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Monte Carlo Simulation — Everything You Need to Know

Monte Carlo Simulations are a series of experiments that help us understand the probability of different outcomes when the intervention of random variables is present. It’s a technique you can use to understand the impact of risk and uncertainty in prediction and forecasting models.

Although Monte Carlo Methods can be studied and applied in many ways, we’re going to focus on:

  • What Monte Carlo Simulation/Method actually is, along with some examples and few experiments.
  • Simulating the game of Roulette with methods of Monte Carlo and inferential statistics.
  • Understanding areas where it finds its application other than gambling.

Why should you learn about Monte Carlo Simulation?

Monte Carlo Simulations utilize something called the Monte Carlo Sampling technique, which is randomly sampling a probability distribution. We’re going to see a simulation based around this technique later in the article. 

Since business and finance are plagued by random variables, Monte Carlo simulations have a vast array of potential applications in these fields. They’re used to estimate the probability of cost overruns in large projects, and the likelihood that an asset price will move in a certain way.

Telecoms use them to assess network performance in different scenarios, helping them to optimize the network. Analysts use them to assess the risk that an entity will default, and to analyze derivatives such as options.

Insurers and oil well drillers also use them. Monte Carlo simulations have countless applications outside of business and finance, such as in meteorology, astronomy, and particle physics.

In machine learning, Monte Carlo methods provide the basis for resampling techniques like the bootstrap method for estimating a quantity, such as the accuracy of a model on a limited dataset.

The bootstrap method is a resampling technique used to estimate statistics on a population by sampling a dataset with replacement.


A mathematician who goes by the name Stanislaw Ulam started playing solitaire while recovering from an illness. He wondered what’s the probability of winning this game. So, he spent a lot of time trying to work the odds and combinatorics of the game, but despite being a great mathematician (he also took part in the Manhattan project), he failed! Which you can understand if you’ve played solitaire. There are tons of combinations which makes the combinatorial calculation super complicated.

To tackle this he thought of going the experimental route, counting the number of hands won / number of hands played. But he had already played a lot of hands without a win, so it would take him years to play enough hands to actually get a good estimate. So instead of playing all the hands, he thought of simulating the game on the computer.

He consulted his computer geek friend John von Neumann, who agreed on running the experiment on his new ENIAC machine, which would take a couple of hours to run the experiments (microseconds for a machine this age). And thus, Monte Carlo Simulation was born!

What is Monte Carlo Simulation?

Monte Carlo Simulation is a method of estimating the value of an unknown quantity using the principles of inferential statistics.

Inferential statistics corresponds to applying statistical algorithms on a sample/random variable, drawn from a sample that tends to exhibit the same properties as the population (from which it is drawn).

Monte Carlo Simulation
Source: original image

In the case of solitaire, the population is the universe of all possible games of solitaire which could be possibly played, and the sample is the games we played (>1). Now, the inferential statistics tells you that we can make inferences about the population based on the statistics we do on the sample. 

Note: The statement of inferential statistics will only hold true when the sample is sampled randomly.

A major part of inferential statistics is decision making. But to make a decision you must know the combinations of outcomes that might occur, and in what variation.

Let’s take an example to understand this better. Let’s say you’re given a coin and you have to estimate the fraction of heads you would get if you flipped the coin a certain number of times. How confident would you be to say that all flips will turn heads? Or that heads will come up the (t+1)th time? Let’s weigh in some possibilities to make it interesting:

  • Flipped a coin 2 times. Both were heads, will you weigh in this if you were to predict the outcome of the 3rd toss?
  • What if there are 100 trials and all come up heads, will you feel more comfortable thinking 101st flip will be heads? (nowhere is it mentioned that it’s a fair coin, might as well be weighted)
  • Now, what if out of 100 trials, 52 came up heads? Will your best guess be 52/100?

For t=2 times you’d be less confident to predict the third one head, and for t=100 you’d be more confident to predict 101st flip to be heads when all are heads than the case where 52 flips were heads.

The answer to this is variance. The confidence in our estimate depends on two things:

  • Size of the sample (100 vs 2)
  • The variance of the sample (all heads vs 52 heads)

As the variance grows, we need larger samples to have the same degree of confidence. When almost half are heads and half are tails, the variance is high, but when the outcome is always heads, the variance is almost 0.

Let’s now see some experiments based on Monte Carlo methods in action.

Monte Carlo Simulation experiments

One of the most popular games that’s common among statisticians and gamblers is Roulette! Of course, for various different reasons we’re gonna see the Monte Carlo simulation in action by running over the game of roulette.

To make this fun, we’re gonna divide this into 3 experiments influenced by each other:

  • Fair Roulette: Expected value for the return of your bets should be 0%.
  • European Roulette: Expected value for the return of your bets should be ~-2.7%
  • American Roulette: Expected value for the return of your bets should be ~-5.5%.

Fair Roulette

Let’s define a simple python class to simulate the game:

import random
class FairRoulette():
   def __init__(self):
       self.pockets = []
       for i in range(1,37):
       self.ball = None
       self.pocketOdds = len(self.pockets) - 1
   def spin(self):
       self.ball = random.choice(self.pockets)
   def betPocket(self, pocket, amt):
       if str(pocket) == str(self.ball):
           return amt*self.pocketOdds
       else: return -amt
   def __str__(self):
       return 'Fair Roulette'
  • There are 36 pockets numbered from 1–36
  • Initially, the ball is at None. The wheel is spun and the ball lands on one of these pockets randomly
  • If the pocket number on which the ball landed matches with the number you bet on priorly, you win the game. So if you bet $1 at the start and you win the hand then you end up with $36. Amazing, isn’t it?

Now a function to put all instructions and rules together:

def playRoulette(game, numSpins, pocket, bet, toPrint):
   totalPocket = 0
   for i in range(numSpins):
       totalPocket += game.betPocket(pocket, bet)
   if toPrint:
       print(f'{numSpins} spins of {game}')
       print(f'Expected return betting {pocket} = {str(100*totalPocket/numSpins)}% \n')
   return (totalPocket/numSpins)

The code is pretty much self-explanatory. totalPocket is a variable that keeps the sum of the money you won/lost. numSpins is how many times you placed a bet or spun the wheel.

Now let’s run this thing for 100 and 1 million spins.

game = FairRoulette()
for numSpins in (100, 1000000):
   for i in range(3):
       playRoulette(game, numSpins, 2, 1, True)
Fair rulette results

You can see in the image above

  • For 100 spins you’ve got a 44% positive return followed by a negative 28%, and again a positive 44%, much higher variance but things get interesting when you look at the bigger picture.
  • For a million spins the returns always hover around the mean 0 which is what we started with. The variance is low.

One must be wondering why bother with a million spins? A gambler might not, but a casino surely will. For a gambler, it’s harder to predict how much he’ll win or lose that day, given that he can play 100 games on that particular day. But a casino has to run for years with millions of gamblers playing tens of millions of hands.

So what’s going on with the experiment is something called the law of large numbers. According to the law of large numbers:

the average of the results obtained from a large number of trials should be close to the expected value and will tend to become closer to the expected value as more trials are performed.

That means if we spun the roulette wheel an infinite number of times, the expected return would be zero. A million is closer to the infinite than 100, so we got the result much closer to zero when spun a million times than a hundred.

This law is often miscued with any game of chance. Take an example of a fair coin. If 10 consecutive heads appear (highly unlikely, p=1/1024) then a person would think the next 10 flips would result in >5 tails (more tails than heads) just to even things out. This is called Gambler’s Fallacy. A more constructive approach to look at this would be regression to the mean, which says “following an extreme event (highly unlikely), the next random event is likely to be less extreme”. In the coin case, it’s likely that in the next 10 flips you’ll get fewer than 10 heads followed by an extreme heads event. 

So, if you look at the average of 20 flips, it will be closer to the expected mean of 50% heads than to the 100% of the first 10 flips.

Let’s get back to our roulette experiment, and now run European and American roulette. You see, casinos are not in the business of being fair. After all, they have to stick up to the phrase “the house always wins!”. In the case of European roulette, they add an additional “0” to the wheel in green apart from the rest red/black, and in American they sneak in another “00” in green, making the odds tougher for a gambler to win.

Let’s simulate both and compare them to the Fair roulette simulations.

class EuRoulette(FairRoulette):
   def __init__(self):
   def __str__(self):
       return 'European Roulette'
class AmRoulette(EuRoulette):
   def __init__(self):
   def __str__(self):
       return 'American Roulette'

We just inherited the FairRoulette class, as the functions and attributes remain the same with additional pockets which we appended. Now a function to run the trials and save the returns:

def findPocketReturn(game, numTrials, trialSize, toPrint):
   pocketReturns = []
   for t in range(numTrials):
       trialVals = playRoulette(game, trialSize, 2, 1, toPrint)
   return pocketReturns

In order to compare, we spin the wheel for all three and see the average returns for 20 trials of each.

numTrials = 20
resultDict = {}
games = (FairRoulette, EuRoulette, AmRoulette)
for G in games:
   resultDict[G().__str__()] = []
for numSpins in (1000, 10000, 100000, 1000000):
   print(f'\nSimulate, {numTrials} trials of {numSpins} spins each')
   for G in games:
       pocketReturns = findPocketReturn(G(), numTrials,
                                        numSpins, False)
       expReturn = 100*sum(pocketReturns)/len(pocketReturns)
       print(f'Exp. return for {G()} = {str(round(expReturn, 4))}%')

Running the above excerpt will get you this:

Fair rulette results

You can see here that as we move closer to infinity, return for each game gets closer to their expected value. So if you play long you are most likely to lose ~3% in European, and ~5% in American. Told you, casinos are in the business of sure shot profit.

Note: Whenever you’re sampling randomly, you aren’t guaranteed to get perfect accuracy. It’s always possible to get a weird sample and that’s one of the reasons we averaged over 20 trials.

You’ve reached the end!

You now understand what a Monte Carlo Simulation, and how to perform one. We only took the example of roulette, but you can experiment on other games of chances. Visit the notebook here to find the complete code in this blog and one bonus experiment we did for you!


ML Experiment Tracking: What It Is, Why It Matters, and How to Implement It

Jakub Czakon | Posted November 26, 2020

Let me share a story that I’ve heard too many times.

”… We were developing an ML model with my team, we ran a lot of experiments and got promising results…

…unfortunately, we couldn’t tell exactly what performed best because we forgot to save some model parameters and dataset versions…

…after a few weeks, we weren’t even sure what we have actually tried and we needed to re-run pretty much everything”

– unfortunate ML researcher.

And the truth is, when you develop ML models you will run a lot of experiments.

Those experiments may:

  • use different models and model hyperparameters
  • use different training or evaluation data, 
  • run different code (including this small change that you wanted to test quickly)
  • run the same code in a different environment (not knowing which PyTorch or Tensorflow version was installed)

And as a result, they can produce completely different evaluation metrics. 

Keeping track of all that information can very quickly become really hard. Especially if you want to organize and compare those experiments and feel confident that you know which setup produced the best result.  

This is where ML experiment tracking comes in. 

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