  One example of chance without randomness involves an unbiased urn where balls are drawn without replacement. Each draw (with the exception of the last) is an event which happens by chance, but the sequence of outcomes will not be random (because the first half of the sequence will carry significant information about the composition of the second half, which may aid compressibility). But a more compelling example is found in stochastic processes in which the chances of future outcomes depend on past outcomes. One well-known class of such process are Markov chains, which produce a discrete sequence of outcomes with the property that the value of an outcome is dependent on the value of the immediately prior outcome (but that immediately prior outcome screens off the rest of the history). A binary Markov chain might be the weather (Gates and Tong 1976): if the two possible outcomes are ‘sunny’ and ‘rainy’, it is plausible to suppose that whether tomorrow is rainy depends on whether today was rainy (a rainy day is more likely to be preceded by another rainy day); but knowing that today was rainy arguably makes yesterday’s weather irrelevant.