Probabilistic programming is a powerful high-level paradigm for probabilistic modeling and inference. We present Odds, a small domain-specific language (DSL) for probabilistic programming, embedded in Scala. Odds provides first-class support for random variables and probabilistic choice, while reusing Scala's abstraction and modularity facilities for composing probabilistic computations and for executing deterministic program parts. Odds accurately represents possibly dependent random variables using a probability monad that models committed choice. This monadic representation of probabilistic models can be combined with a range of inference procedures. We present engines for exact inference, rejection sampling and importance sampling with look-ahead, but other types of solvers are conceivable as well. We evaluate Odds on several non-trivial probabilistic programs from the literature and we demonstrate how the basic probabilistic primitives can be used to build higher-level abstractions, such as rule-based logic programming facilities, using advanced Scala features.