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Let $S$ be the set of all probability distributions on $\mathbb{R}$ and $S_n=\{p_\theta\}$ be an $n$ dimensional submanifold of parameterized family of probability distributions on $\mathbb{R}$ where $\theta=(\theta_1,\dots, \theta_n)\in \Theta$ are called parameters of the probability density $p_\theta(x)$ and $\Theta$ is an open set homeomorphic to $\mathbb{R}^n$. It is also assumed that $p_\theta(x)>0$ for all $x$.

For example, $$p_{\theta=(\mu,\sigma)}(x)=\frac{1}{\sqrt{2\pi}\sigma}e^{\frac{-(x-\mu)^2}{2\sigma^2}}$$ where $\mu\in \mathbb{R},\sigma>0$ forms a $2$-dimensional submanifold.

On this manifold, a Riemannian metric is defined by the following, called Fisher information metric.

\begin{eqnarray*} g_{i,j}(\partial_i,\partial_j) &=&\int \partial_i \log p_\theta(x) ~\partial_j \log p_\theta(x) ~p_\theta(x)~ dx\\ &=& \int \partial_i p_\theta(x) ~\partial_j \log p_\theta(x)~ dx \end{eqnarray*}

where $\partial_i=\frac{\partial}{\partial \theta_i}$.

My question is regarding a point in page 384 from this Ann. Stat. paper by Amari.

In the above mentioned paper, when he says a curve $\{q_t(x)\}$ in $S$, where $q_0(x)=p_\theta(x)$ intersects orthogonally $S_n$ at $q_0(x)=p_\theta(x)$, he means

$$\int \partial_i \log p_\theta(x) ~\frac{d}{dt} \log q_t(x)\lvert_{t=0} ~q_0(x)~dx=0$$. i.e., $$\int \partial_i p_\theta(x) ~\frac{d}{dt} \log q_t(x)\lvert_{t=0} ~dx=0$$.

Why? Could somebody make me understand by giving intuitive explanation of these concepts?

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