Consider the following set up. Let $0 < a < b < \infty$ be fixed constants, and $G \subset \mathbb{R}^{d}$ be a compact set, for some fixed $d \in \mathbb{N}$.
Then define $$\mathcal{H}_{a}^{b} := \left\{h \colon G \to [a, b] \mid \int_{G} h d \mu = 1, h \text{ measurable} \right\},$$ to be the set of uniformly bounded probability densities on $G$.
Further let us endow $\mathcal{H}_{a}^{b}$ with the $L_{2}$-metric, i.e., for all $f_1, f_2 \in \mathcal{H}_{a}^{b}$ $$\|f_1 - f_2\|_{2} := \left(\int_{G} (f_1 - f_2)^{2} d\mu \right)^{1 / 2}$$
Let $\mathcal{E} \subset \mathcal{H}_{a}^{b}$, denote a closed and convex set. We will also consider $\mathcal{E}$ to be a restricted $L_{2}$-metric space of $\mathcal{H}_{a}^{b}$.
I want to understand basic topological properties of $\mathcal{E}$ and $\mathcal{H}_{a}^{b}$. All of these are with respect to the $L_{2}$-metric:
- Is $\mathcal{H}_{a}^{b}$ compact?
- If not compact, is $\mathcal{H}_{a}^{b}$ totally bounded?
- If not totally bounded, is $\mathcal{H}_{a}^{b}$ separable?
- Since $\mathcal{H}_{a}^{b}$ is bounded (by $2b$), $\mathcal{E}$ is bounded. Further $\mathcal{E}$ is also closed and convex by assumption. Do these 3 properties guarantee either compactness, total boundedness, separability of $\mathcal{E}$?
I am able to show that $\mathcal{H}_{a}^{b}$ is convex, and also bounded, but not the above properties. The properties can imply each other in the metric space setting, so if the most general property is proven, then it would solve many of these queries efficiently, e.g., compactness of $\mathcal{H}_{a}^{b}$ should imply total boundedness and thus separability of $\mathcal{H}_{a}^{b}$ (I believe).
Could anyone rigorously justify these, or provide counterexamples?