Monte Carlo Simulation for Intuition
Answer tricky probability and planning questions by simulating them thousands of times
A taste of a lesson
Our project has 5 tasks. Adding up the most likely durations gives 40 days. Is 40 days a safe estimate?
Usually not. Task durations tend to have long right tails: a task can rarely finish much early but can run very late. When you add several such tasks, the total is more likely to exceed the sum of most likely values than fall below it. Simulate it: give each task a minimum, most likely and maximum, draw random durations 10,000 times, and sum each run. Then look at the 50th and 90th percentiles. Before simulating, guess: what percentage of runs will finish within 40 days?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Estimate probabilities by simulating random experiments
- Explain how the number of runs affects precision
- Simulate project timelines and report percentiles
- Write down and test the assumptions behind a simulation
- Show how correlated inputs change the spread of outcomes
Lesson plan
- 1 Guess, then simulate Estimate a simple probability by repeated random trials. Start
- 2 How many runs Understand how precision grows with the number of runs. Start
- 3 The birthday problem Resolve a famous counterintuitive result by simulation. Start
- 4 Project timelines Simulate total duration from uncertain task estimates. Start
- 5 Correlated inputs See how shared causes widen outcome ranges. Start
- 6 Assumptions and checks Make simulations trustworthy and reproducible. Start
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About this tutor
A beginner tutor that teaches simulation as a practical thinking tool. You will answer probability questions by repeating random experiments many times, first with dice and coins, then with realistic problems like project timelines with uncertain tasks, inventory with variable demand and the famous birthday problem. Lessons explain why more simulations give more precise answers, how to read percentiles of outcomes, why input assumptions matter more than the number of runs, and how correlations between inputs change results. You can follow along in a spreadsheet or a few lines of code.
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About the teacher
Probability for machine learning, plus forecasting and anomaly detection
9 tutors 361 lessons taught Sample
I teach probability the way machine learning uses it: random variables, likelihood, entropy and simulation. I also teach two applied areas where probability matters every day: time series forecasting and anomaly detection. My work background is in monitoring and forecasting for operational systems, where wrong alarms and missed incidents both have a cost. I teach through small simulations, coin and...
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