This question is motivated by this posting.
In the classic book Meyn, S. and Tweedie, R., Markov Chains and Stochastic Stability, the authors introduce the concept of a spread out distribution:
Definition: A probability measure $\mu$ on $(\mathbb{R},\mathscr{B}(\mathbb{R}))$ is a spread out distribution if there is $n\in\mathbb{N}$ such that $\mu^{*n}$ has a nonzero absolutely continuous (w.r.t. Lebesgue measure).
Here $\mu^{*n}$ denotes the convolution of $\mu$ with itself $n$-times. These measures come as examples of random walks where ergodicity properties can be analyze in terms of small sets.
Problem: Any measure $\nu$ with non trivial absolutely continuous part is of course spread out ($n=1$). What I am asking (out of curiosity and not for professional need) is for an example of a purely singular measure $\nu$ (w.r.t Lebesgue measure) that is spread out in the sense above.
My first instinct was look for example, the 1/3-Cantor measure $\mu_{1/3}$. Yuval Peres, here show me that this measure (and other fractal-like measures) preserve singularity under self-convolutions. Thus, such $\nu$ must be either too easy to obtain for my myopic eyes, or much more sophisticated.