1. Learn
    2. /
  2. Courses
    3. /
  3. Monte Carlo Simulations in Python
  • 1

    Introduction to Monte Carlo Simulations

    Free

    What are Monte Carlo simulations and when are they useful? After covering these foundational questions, you’ll learn how to perform simple simulations such as estimating the value of pi. You’ll also learn about resampling, a special type of Monte Carlo Simulation.

  • 2

    Foundations for Monte Carlo

    Now that you can run your own simple simulations, you’re ready to explore real-world application of Monte Carlo simulations across various industries. Then, you’ll dive into the heart of what makes a good simulation work: sampling from the correct probability distribution. You’ll learn about probability distributions for discrete, continuous, and multivariate random variables.

  • 3

    Principled Monte Carlo Simulation

    Once you’re comfortable with your choice of probability distribution, you’re ready to follow a principled Monte Carlo simulation workflow using a dataset of diabetes patient characteristics and outcomes. You will explore the data, perform a simulation, and generate summary statistics to communicate your simulation results.

  • 4

    Model Checking and Results Interpretation

    Discover how to evaluate your Monte Carlo models and communicate the results with easy-to-read visualizations in Seaborn. Finally, use sensitivity analysis to understand how changes to model inputs will impact your results, and practice this concept by simulating how business profits are impacted by changes to sales and inflation!

Initializing

Exercise

The Law of Large Numbers

You learned in the previous exercise that due to the stochastic nature of Monte Carlo simulations, each simulation result can be very different. In this exercise, you'll leverage the Law of Large Numbers to simulate inflation in 2050 based on the average of a large number of simulations.

The monte_carlo_inflation() function you wrote in the previous exercise is available for use. As a reminder, this is the function code:

def monte_carlo_inflation(year, seed):
    random.seed(seed)
    inflation_rate = 8.6
    yearly_increase = random.randint(1, 3)
    for i in range(year - 2022):
        inflation_rate = inflation_rate * ((100 + yearly_increase)/100)
    return(inflation_rate)

The numpy and random packages have been imported for you.

Instructions

100 XP
  • Calculate the average of 1,000 simulations where a seed between 0 and 20,000 is randomly chosen each time.
  • Calculate the average of 10,000 simulations where a seed between 0 and 20,000 is randomly chosen each time.