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Bayesian Solution for Monty Hall’s Paradox

Given that the guest chooses door 1 and the host opens door 3, we apply Bayesian probability to find the probability that the car is behind door 1:

P(1=car3=opened)=P(3=opened1=car)P(1=car)P(3=opened)P(1=\text{car}|3=\text{opened}) = \frac{P(3=\text{opened}|1=\text{car}) \cdot P(1=\text{car})}{P(3=\text{opened})}

P(3=opened)P(3=\text{opened}) is the total probability that the host opens door 3 when the guest picked door 1:

P(3=opened)=P(3=opened1=car)P(1=car)+P(3=opened2=car)P(2=car)+P(3=opened3=car)P(3=car)P(3=\text{opened}) = P(3=\text{opened}|1=\text{car}) \cdot P(1=\text{car}) + P(3=\text{opened}|2=\text{car}) \cdot P(2=\text{car}) + P(3=\text{opened}|3=\text{car}) \cdot P(3=\text{car})

P(3=opened)=1213+113+013=12P(3=\text{opened}) = \frac{1}{2} \cdot \frac{1}{3} + 1 \cdot \frac{1}{3} + 0 \cdot \frac{1}{3} = \frac{1}{2}

Substituting this back into the first equation:

P(1=car3=opened)=121312=1612=13P(1=\text{car}|3=\text{opened}) = \frac{\frac{1}{2} \cdot \frac{1}{3}}{\frac{1}{2}} = \frac{\frac{1}{6}}{\frac{1}{2}} = \frac{1}{3}

Hence, the probability that the car is behind door 1 is 13\frac{1}{3}, given the guest selected door 1 and the host opened door 3.

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Updated 2026-06-16

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Data Science