A little help understanding this bayesian problem (very basic)

  • Thread starter bennyska
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In summary, the author is stuck on the first equation of the homework and is trying to figure out what he is doing wrong.
  • #1
bennyska
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Homework Statement


so I'm trying to teach myself bayes, and i got a book, and I'm going through trying to do the exercises, and lo and behold, i get stuck on the first one. i thought i was getting it, but the answer given at the back of the book is different than mine.



Homework Equations





The Attempt at a Solution


i've attached three pictures. the first is the actual problem. so when i did it myself, i got that the first column of priors should all be 1/10, since we're assuming that the urn can contain 0 up to 9 red balls, for a total of 10 possibilities. the second picture is the answer, where it seems that i should have said 1/9 for the first column. the third picture is an example that seems to me to indicate that it should be 1/10. what am i missing?
 

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  • #2
There are 10 possible values for X, but there are only 9 balls to choose from.
What does the prior probability represent in this case?
 
  • #3
the prior probability represents the number of red balls in the urn. I'm told to assume that each possibility is equally likely. so there might be zero balls, 1 ball, 2 balls, etc, up to 9 balls. that should be a 1/10 probability for each case. furthermore, shouldn't the prior column sum up to 1? in the answer, doesn't the prior column sum up to 10/9?
 
  • #4
bennyska said:
the prior probability represents the number of red balls in the urn. I'm told to assume that each possibility is equally likely. so there might be zero balls, 1 ball, 2 balls, etc, up to 9 balls. that should be a 1/10 probability for each case. furthermore, shouldn't the prior column sum up to 1? in the answer, doesn't the prior column sum up to 10/9?

You are correct. Either the "zero balls" case should not be present (giving 1/9 probability of each of the others) or else it is present and all priors should be 1/10.

RGV
 
  • #5
thank you. also, i should have been more clear, the third picture was an example with the exact same problem, just fewer balls.
 
  • #6
Yes - I'm inclined to agree that the text is mistaken in the model answer.
Frustrating, I know.

I had a go trying to identify the mistake but failed. Why would the author think to assume that P(X=x)=x/(N-1)?

I'd ignore the answers but do the analysis anyway. Pause at each step to see if what you get makes sense. The other one seems to be right - maybe you want to start with that instead.
 

Related to A little help understanding this bayesian problem (very basic)

1. What is Bayesian probability?

Bayesian probability is a type of statistical inference that uses Bayes' theorem to update the probability of a hypothesis as more evidence or information becomes available.

2. How is Bayesian probability different from traditional probability?

Traditional probability is based on the long-run frequency of an event occurring, while Bayesian probability takes into account prior knowledge and updates the probability as new information is obtained.

3. What is a prior probability?

A prior probability is an initial belief about the likelihood of an event occurring, before any new evidence is taken into account.

4. How is a prior probability used in Bayesian inference?

In Bayesian inference, the prior probability is combined with new evidence using Bayes' theorem to obtain a posterior probability, which is the updated belief about the likelihood of an event occurring.

5. What is the importance of Bayesian probability in scientific research?

Bayesian probability allows scientists to incorporate prior knowledge and update their beliefs as new evidence is obtained, making it a powerful tool for making predictions and drawing conclusions in various fields of research.

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