Building Monte Carlo Loops
Monte Carlo simulation is a powerful technique for modeling uncertainty in complex systems. At its core, the Monte Carlo method uses repeated random sampling to estimate the probability of certain outcomes, especially when analytical solutions are difficult or impossible to obtain. In uncertainty modeling, you can use Monte Carlo simulations to explore the likelihood of different results, understand risk, and make informed decisions based on simulated data rather than theoretical assumptions.
1234567891011121314151617import random # Number of simulation runs num_runs = 10000 success_count = 0 for _ in range(num_runs): # Simulate 10 coin flips flips = [random.choice(['H', 'T']) for _ in range(10)] heads = flips.count('H') # Check if heads occur at least 60% of the time (i.e., 6 or more) if heads >= 6: success_count += 1 # Estimate the probability probability = success_count / num_runs print(f"Estimated probability of getting heads at least 60% of the time in 10 flips: {probability:.4f}")
The structure of a Monte Carlo simulation typically involves running a large number of trials, or "runs," to capture the variability of random processes. In the previous code, the loop repeats the experiment 10,000 times, representing independent trials of flipping a coin 10 times. Within each run, the code simulates the random process—in this case, flipping a fair coin 10 times and counting the number of heads. After each run, the outcome is checked to see if it meets the condition of interest (at least 60% heads), and a counter is incremented accordingly. Once all runs are complete, the code aggregates the results by dividing the number of successful runs by the total number of runs to estimate the probability of the event. This aggregation provides a robust estimate of the likelihood of the outcome under uncertainty.
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Monte Carlo simulation is a powerful technique for modeling uncertainty in complex systems. At its core, the Monte Carlo method uses repeated random sampling to estimate the probability of certain outcomes, especially when analytical solutions are difficult or impossible to obtain. In uncertainty modeling, you can use Monte Carlo simulations to explore the likelihood of different results, understand risk, and make informed decisions based on simulated data rather than theoretical assumptions.
1234567891011121314151617import random # Number of simulation runs num_runs = 10000 success_count = 0 for _ in range(num_runs): # Simulate 10 coin flips flips = [random.choice(['H', 'T']) for _ in range(10)] heads = flips.count('H') # Check if heads occur at least 60% of the time (i.e., 6 or more) if heads >= 6: success_count += 1 # Estimate the probability probability = success_count / num_runs print(f"Estimated probability of getting heads at least 60% of the time in 10 flips: {probability:.4f}")
The structure of a Monte Carlo simulation typically involves running a large number of trials, or "runs," to capture the variability of random processes. In the previous code, the loop repeats the experiment 10,000 times, representing independent trials of flipping a coin 10 times. Within each run, the code simulates the random process—in this case, flipping a fair coin 10 times and counting the number of heads. After each run, the outcome is checked to see if it meets the condition of interest (at least 60% heads), and a counter is incremented accordingly. Once all runs are complete, the code aggregates the results by dividing the number of successful runs by the total number of runs to estimate the probability of the event. This aggregation provides a robust estimate of the likelihood of the outcome under uncertainty.
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