Weak convergence of probability measures on weak versus strong dual

The space of temperate distributions $S'(\mathbb{R}^d)$ is often equipped with the weak-$\ast$ or with the strong topology. When defining the notion of a probability measure on $S'(\mathbb{R}^d)$, this makes no difference since the corresponding Borel $\sigma$-algebras are the same: see for instance this article by Becnel.

However, when it comes to weak convergence of probability measures, the topology cannot be ignored. Even though $S'(\mathbb{R}^d)$ is not a Polish space, I am using the standard definition: $\mu_n\rightarrow \mu$ weakly if for all bounded continuous functions $F:S'(\mathbb{R}^d)\rightarrow \mathbb{R}$ one has $$\lim_{n\rightarrow\infty} \int F\ d\mu_n\ =\ \int F\ d\mu\ .$$ My question is: does weak convergence for the weak-$\ast$ topology imply weak convergence for the strong topology?

The reason I ask is that the strong topology seems to be the preferred one in the literature on topological vector spaces. On the other hand, the weak-$\ast$ topology is very nice as far as probability theory goes, in particular Prokhorov's Theorem and Levy's Continuity Theorem hold for it.