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We are interested in a probability distribution $\rho:X \rightarrow \mathbb R_+$, such that | |
1. (Constraints) $ d_i^{-1} (\rho d\mathcal L ) = \mathbb P_i$, i.e. $\forall B \subseteq \mathbb R $ we have $\int 1_{\{d_i(x) \in B \} }\rho(x) d \mathcal L(x) = \mathbb P_i(B)$. | |
2. (Optimization problem) $\int \rho \log \rho d\mathcal L$ extremal - this expression maximizes entropy under the constraints. This takes account to the fact that less is known about the actual distribution of the probability of presence. | |
To assure positivity we set $\rho = e^\varphi$ and obtain $\rho \log \rho = \varphi e^\varphi$ for the entropy. For simplicity we assume for $\mathbb P_i$ only discrete probabilities $\mathbb P_i = \sum_k p_{i,k} \delta_{x_{i,k}}$ | |
The Gâteaux differential $D^{Gâteaux}$ in direction $\psi$ is given by $\partial_h F(\varphi + h\psi)\mid_{h=0}$ |
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#A probability distribution (updated) | |
### suggested by Tilman J. Bohl | |
We are interested in a probability distribution $\rho:X \rightarrow \mathbb R_+$, such that | |
1. (Constraints) $ d_i^{-1} (\rho d\mathcal L ) = \mathbb P_i$, i.e. $\forall B \subseteq \mathbb R $ we have $\int 1_{\{d_i(x) \in B \} }\rho(x) d \mathcal L(x) = \mathbb P_i(B)$. | |
2. (Optimization problem) $\int \rho \log \rho d\mathcal L$ extremal - this expression maximizes entropy under the constraints. This takes account to the fact that less is known about the actual distribution of the probability of presence. | |
To assure positivity we set $\rho = e^\varphi$ and obtain $\rho \log \rho = \varphi e^\varphi$ for the entropy. For simplicity we assume for $\mathbb P_i$ only discrete probabilities $\mathbb P_i = \sum_k p_{i,k} \delta_{x_{i,k}}$ |