Bayesian Statistics: Bernoulli example

I am a bit confused here specifically why theta is a continuous random variable?

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Hi @Musab_Bin_Gulfam,

\theta shows the probability of success, and although the outcomes (0 and 1) are discrete, \theta is treated as a continuous random variable here.

We use a prior distribution to represent our initial uncertainty about \theta, and after observing data, we update this to a posterior distribution, which is the probabilities of different \theta values.

Hope this helps, feel free to ask if you need further help!

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Thank you.

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You’re welcome! happy to help :raised_hands:

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