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The Fisher information matrix (in the scalar parameter case) can be obtained from the Kullback-Leibler divergence by $$g(\theta) = -\frac{\partial}{\partial \theta}\frac{\partial}{\partial \theta'}D(...

Let $(\Omega,\Sigma,\mu)$ be a probability space and let $\mathcal{M}$ be an exponential family of probability distributions for $\mu$ of the following form: There are $\varphi_1,\dots,\varphi_n:\...

Let us define the arithmetic, geometric, and harmonic means of $x,y \in \mathbb{R}$ weighted by $\alpha =(\alpha_x,\alpha_y) \in [0,1]$, respectively as \begin{equation} a_\alpha(x,y) = \frac{\...

Suppose I have Markov chains: $$X \rightarrow f(X) \rightarrow g(X)$$ $$Y \rightarrow f(Y) \rightarrow g(Y)$$ where it is known that minimizing the $\mathbb{E}(g(X)) - \mathbb{E}(g(Y))$ minimizes the ...

Given normal distributions with a single positional and variation parameter each, $p_1=\big[\mu_1, \sigma_1\big]$, $p_2=\big[\mu_2, \sigma_2\big]$, we define their Jensen-Shannon divergence as: $$ \...

Prefacing apology for likely having unclear notation in the question and possible unclear concepts, because I'm not a mathematician. The Fisher Information Matrix (FIM) for a multivariate normal ...