The likelihood ratio plays an important role in statistical theory and also in practice. In applied contexts, it is often assumed that a pair of probability distributions satisfies a likelihood ratio order. Statistical inference is challenging in this setting due to the likelihood ratio constraint. To address this problem, we present a novel characterization of the likelihood ratio order in terms of mixtures which allows for unconstrained inference. We illustrate its utility in Bayesian nonparametric density estimation and hypothesis testing.