An important aspect of probabilistic inference in embedded real-time systems is flexibility to handle changes and limitations in space and time resources. We present algorithms for probabilistic inference that focus on simultaneous adaptation with respect to these resources. We discuss techniques to reduce memory consumption in Bayesian network inference, and then develop adaptive conditioning, an anyspace anytime algorithm that decomposes networks and applies various algorithms at once to guarantee a level of performance. We briefly describe adaptive variable elimination, an anyspace algorithm derived from variable elimination. We present tests and applications with personal digital assistants and industrial controllers.