  We present a new construction of a Skorohod embedding, namely, given a
probability measure mu with zero expectation and finite variance, we construct
an integrable stopping time T adapted to a filtration F_t, such that W_t has
the law mu, where W_t is a standard Wiener process adapted to the same
filtration. We find several sufficient conditions for the stopping time T to be
bounded or to have a sub-exponential tail. In particular, our embedding seems
rather natural for the case that mu is a log-concave measure and the tail
behaviour of $T$ admits some tight bounds in that case. Our embedding admits
the property that the stochastic measure-valued process {mu_t} (0<t<T), where
mu_t is as the law of W_T conditioned on F_t, is a Markov process.
