Maximum of entropy and Lagrange multiplier

AI Thread Summary
The discussion focuses on finding the probability density function that maximizes entropy under specific constraints. The entropy is defined as S = -∫ ρ(x) ln(ρ(x)) dx, with constraints on the mean and normalization of the density function. The Lagrangian formulation is used to derive the expression for ρ(x), leading to the form ρ(x) = e^{-(1 + λ1 + x λ2)}. Normalization is applied to ensure that the total probability integrates to one, resulting in ρ(x) = e^{-x λ2}/∫ e^{-x λ2} dx. The user seeks further guidance on simplifying or improving this expression.
Nico045
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Hello, I have to find the density of probability which gives the maximum of the entropy with the following constraint\bar{x} = \int x\rho(x)dx
\int \rho(x) dx = 1

the entropy is : S = -\int \rho(x) ln(\rho(x)) dx

L = -\int \rho(x) ln(\rho(x)) dx - \lambda_1 ( \int \rho(x) dx -1 ) - \lambda_2 (\int x \rho(x)dx -\bar{x})

\frac {\partial L } { \partial \rho(x) } = \int ( - ln(\rho(x)) -1 - \lambda_1 - x \lambda_2 ) dx= 0

\rho(x) = e^{-(1 + \lambda_1 + x \lambda_2)}

Now I use the normalisation

\int \rho(x) dx = 1 = e^{-(1 + \lambda_1) } \int e^{-x\lambda_2} dx \Rightarrow e^{-(1 + \lambda_1) } = \frac{1}{\int e^{-x\lambda_2} dx}

\rho(x) = \frac{e^{-x \lambda_2}}{\int e^{-x\lambda_2} dx}

From there I don't really know what to do. What shall I do to get a better expression of this ?
 
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