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MathJax: \exp and \ln
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Martin Sleziak
Martin Sleziak
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I have the joint PMF

$exp(y_1 ln(\lambda)+y_2 ln(c)+y_2ln(\lambda)-ln(y_1!y_2!)-\lambda(1+c))$$\exp(y_1 \ln(\lambda)+y_2 \ln(c)+y_2\ln(\lambda)-\ln(y_1!y_2!)-\lambda(1+c))$

for a constant $c>0$. In canonical representation and mixed parameterization I have $\mathbf{\theta}=(ln(c),ln(\lambda))$$\mathbf{\theta}=(\ln(c),\ln(\lambda))$ and $\mathbf{t}=(v,u)^T$ with $v=Y_2$ and $u=Y_1+Y_2$.

What is the marginal PMF $f(v)$?

I have the joint PMF

$exp(y_1 ln(\lambda)+y_2 ln(c)+y_2ln(\lambda)-ln(y_1!y_2!)-\lambda(1+c))$

for a constant $c>0$. In canonical representation and mixed parameterization I have $\mathbf{\theta}=(ln(c),ln(\lambda))$ and $\mathbf{t}=(v,u)^T$ with $v=Y_2$ and $u=Y_1+Y_2$.

What is the marginal PMF $f(v)$?

I have the joint PMF

$\exp(y_1 \ln(\lambda)+y_2 \ln(c)+y_2\ln(\lambda)-\ln(y_1!y_2!)-\lambda(1+c))$

for a constant $c>0$. In canonical representation and mixed parameterization I have $\mathbf{\theta}=(\ln(c),\ln(\lambda))$ and $\mathbf{t}=(v,u)^T$ with $v=Y_2$ and $u=Y_1+Y_2$.

What is the marginal PMF $f(v)$?

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Orongo
Orongo
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Marginal probability mass function

I have the joint PMF

$exp(y_1 ln(\lambda)+y_2 ln(c)+y_2ln(\lambda)-ln(y_1!y_2!)-\lambda(1+c))$

for a constant $c>0$. In canonical representation and mixed parameterization I have $\mathbf{\theta}=(ln(c),ln(\lambda))$ and $\mathbf{t}=(v,u)^T$ with $v=Y_2$ and $u=Y_1+Y_2$.

What is the marginal PMF $f(v)$?