Maximizing θ with Probability Mass Function and Marbles Data

AI Thread Summary
The discussion centers on maximizing the parameter θ using a probability mass function related to four types of marbles: green, blue, red, and white. The data from 3839 randomly picked marbles indicates specific counts for each color, leading to questions about the appropriate likelihood function. Participants clarify that a multinomial distribution is more suitable for this scenario due to the multiple outcomes, rather than a binomial distribution. The process involves setting up the log likelihood function, differentiating it with respect to θ, and solving for the maximum likelihood estimate. The final step includes verifying that the solution corresponds to a maximum by checking the second derivative.
icedsake
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in need of help for how to do this question
given probability mass function:
x 1 2 3 4
p(x) 1/4(θ+2) 1/4(θ) 1/4(1-θ) 1/4(1-θ)

Marbles
1=green
2=blue
3=red
4=white

For 3839 randomly picked marbles
green=1997
blue=32
red=906
white=904

what is the max likelihood of θ using this data?
 
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What is the likelihood function in this case?
 
oops i left out that x=1,2,3,4 are of binomial distributions...
would the likelihood function be the pmf of binomial dist.?
= (nCx) p^x (1-p)^(n-x)

and the loglikelihood function be:
L(p)= log(nCx) + xlog(p) + (n-x)log(1-p) ??
 
Is it a binomial, or a multinomial distribution? Binomial has two possible outcomes; here you have four.
 
i'm a little lost at this point, in the above section it says that for example green marbles is modeled by a r.v. N1 with a binomial (n, 1/4(θ+2)) distribution and blue is modeled by r.v. N2 with a binomial (n,1/4(θ)) dist. where n in both cases is total # of marbles (3839 in this case)

so I'm assuming red and white have similar binomial dist.
 
It is possible to look at multinomial r.v.'s as a vector of binomial r.v.'s.

The likelihood function (nCx) p^x (1-p)^(n-x) represents just one of the 4 variables, though (e.g., green vs. not green). To capture all individual colors you need to think in terms of a multinomial distribution with multiple (> 2) outcomes.
 
hmm..so in this case i should use the multinomial prob. mass function to get the likelihood function.. then take the natural log of it correct?
Do I differentiate now and how do I arrive at the estimate for theta?
 
You should set up the log likelihood function L, then differentiate it with respect to theta, set it to zero, and solve for theta: L'(θ) = 0 so θ* = L'-1(0). Then check L"(θ*) < 0 to make sure it's a maximum and not a minimum.
 
thanks for the clarifications =)
 
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