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

\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!

Thank you.

You’re welcome! happy to help

Hi Alireza:

Thanks for your answer, I encountered this as well in this lesson. So I’m clear, P(H) is a random variable a priori only, and becomes a discrete variable a posteriori? This seems pretty critical, just want to make sure I’m grasping the concept.

Thanks,

Tom

Hi @tfoss78 ,

P(H) is treated as a continuous random variable **both prior and a posteriori**. Because, we use a prior distribution to show uncertainty about \theta . Then, we update this to a posterior distribution, which still counts \theta as a continuous variable.

Hope it helps! Feel free to ask if you need further assistance.