Bounding distributional errors via density ratios

We present some new and explicit error bounds for the approximation of distributions. The approximation error is quantified by the maximal density ratio of the distribution $Q$ to be approximated and its proxy $P$. This non-symmetric measure is more informative than and implies bounds for the total variation distance. Explicit approximation problems include, among others, hypergeometric by binomial distributions, and (generalized) binomial by Poisson distributions. In many cases we provide both upper and (matching) lower bounds.