MHB Trouble understanding PMF tables

  • Thread starter Thread starter Longines
  • Start date Start date
Click For Summary
The discussion centers around understanding probability mass function (PMF) tables and their application in a textbook question involving a random variable X that follows a binomial distribution. The initial query highlights confusion regarding the use of Bayes' theorem in solving the problem. Participants suggest focusing on the PMF of the binomial distribution for the first part of the question. For the second part, the law of total probability is recommended as a guiding principle. Clarification on these concepts is essential for solving the posed questions effectively.
Longines
Messages
9
Reaction score
0
Hello all, I'm back with another basic probability question:

View attachment 3283

I know that it involves Bayes theorem somewhere, but I don't understand this question at all!

Note: This isn't an assignment question or anything like that, it's just a textbook question that I need help with.

Thank you
 

Attachments

  • JDvJmAJ.png
    JDvJmAJ.png
    20.9 KB · Views: 107
Physics news on Phys.org
Hi,
Let's kick-off with question $(a)$. We have given that $X$ is a random variable such that conditionally on $\{U=u\}$, $X$ is binomial with parameters $(2,u)$.

Do you know the binomial distribution? If you do, you only need to apply it's PMF for question $(a)$.

Hint for question $(b)$: law of total probability.
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

Similar threads

  • · Replies 17 ·
Replies
17
Views
2K
  • · Replies 4 ·
Replies
4
Views
3K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 53 ·
2
Replies
53
Views
2K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 4 ·
Replies
4
Views
2K
  • · Replies 1 ·
Replies
1
Views
1K
  • · Replies 11 ·
Replies
11
Views
2K
  • · Replies 6 ·
Replies
6
Views
2K
  • · Replies 3 ·
Replies
3
Views
2K