network profile
| bio | website | |
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| location | ||
| age | ||
| visits | member for | 1 year, 3 months |
| seen | Feb 6 at 6:12 | |
| stats | profile views | 2 |
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Jan 24 |
accepted | How to test the convexity of mutual information using leading principal minors? |
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Jan 24 |
comment |
How to test the convexity of mutual information using leading principal minors? oic, thank you for your wonderful insights. |
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Jan 23 |
comment |
How to test the convexity of mutual information using leading principal minors? thank you for your explanations, I tried again to solve via a simpler Hessian matrix, as shown in my above edits, but still can't get the solution. Do you have any idea why is it so? |
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Jan 23 |
revised |
How to test the convexity of mutual information using leading principal minors? added 800 characters in body |
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Jan 23 |
asked | How to test the convexity of mutual information using leading principal minors? |
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Jan 18 |
awarded | Commentator |
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Jan 18 |
comment |
How to differentiate discrete probabilities? Thank you very much for your help, much appreciated |
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Jan 18 |
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How to differentiate discrete probabilities? @joriki: yes. I haven't think in depth about the problem. I'm thinking since the constraint is linear, I can use the elimination of variable method to simplify the constrained optimization problem into an unconstrained optimization problem |
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Jan 18 |
comment |
How to differentiate discrete probabilities? @joriki: thank you very much for your help. I will be careful in the future. Can you elaborate more on the normalization constraint? |
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Jan 18 |
awarded | Scholar |
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Jan 18 |
accepted | How to differentiate discrete probabilities? |
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Jan 18 |
comment |
How to differentiate discrete probabilities? I am so sorry, I try to add more explanations now. =) |
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Jan 18 |
revised |
How to differentiate discrete probabilities? added 139 characters in body |
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Jan 18 |
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How to differentiate discrete probabilities? @joriki: I think your total derivative is what I needed, and with the relationship $p(x) = \sum_c p(x,c)$, $p(c) = \sum_x p(x,c)$, I can plug these relationships into your total derivative to get my gradient |
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Jan 18 |
revised |
How to differentiate discrete probabilities? added 28 characters in body |
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Jan 18 |
comment |
How to differentiate discrete probabilities? @joriki: I apologise for missing the details. So given that I have $p(x)=\sum_c p(x,c)$ (thank you Ilya), I can differentiate $\frac{\partial p(x)}{\partial p(x,c')}=\frac{\partial \sum_c p(x,c)}{\partial p(x,c')}$? |
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Jan 18 |
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How to differentiate discrete probabilities? I only know the $p(x,c)$ is the joint probability of random variable $x,c$. Is there a way to express $p(x),p(c)$ as function of $p(x,c)$? |
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Jan 18 |
revised |
How to differentiate discrete probabilities? edited body |
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Jan 18 |
comment |
How to differentiate discrete probabilities? @Ilya my function is to measure a characteristic of the data, and I am wondering what will be the optimal probabilities to get the maximum of the function, or if given a probability, what is the gradient of the function to reach the maximum of the function, To get the maximum of the function, I am thinking of getting its gradient |
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Jan 18 |
comment |
How to differentiate discrete probabilities? The discrete probabilities are simply obtained from a discrete data. I am not familiar with partial derivatives. My guess is differentiate $ln (p(x)p(c))$ wrt $p(c,x)$ is zero, but it seems that $p(x)p(c)$ and $p(x,c)$ are related, but they are not governed by a formula or equation, so I do not know how to do partial differentiation on them |
