Model two fair independent coin tosses, recording their order. Here denotes heads and tails, andThe power set includes every question expressible as a subset of these four outcomes. The mass comes from the fairness and independence assumptions. These are exact model probabilities, rather than measured toss frequencies.
Let be the event that the first toss is heads and the event that the second toss is heads. ThenThe union means “at least one head,” while the intersection means “both heads.” Their probabilities are and . Counting the union through the inclusion-exclusion principle gives ; alternatively its complement of an event is , of probability .
Four ordered outcomes and the event of at least one head
. Rows record the first toss and columns the second. Each cell has probability under fair independent tosses. The highlighted union includes , , and ; the overlap contains only . Details.On the same sample space, assigning masses to would still make each toss fair: . But , so the tosses would be dependent. The event of at least one head would have probability . Changing the probability measure changes the answer even when the sample space and the single-toss probabilities stay the same.
Define the head-count random variable byThe four elementary outcomes are inputs to this function; its three numerical values are outputs. Each fiber of a function collects all outcomes giving one value. Under the fair independent model, adding the masses in each fiber gives the probability mass functionConsequently the output values are not equally likely, although the inputs are. Using the reduced sample space with these masses gives the same head-count probability distribution; assigning to each value would describe a different model.
A question about a random variable's value becomes an event on the original sample space by taking an inverse image:For a real-valued random variable, this is an event whenever is a Borel set, by measurability. In this finite example every subset of is allowed. In particular,This is the same “at least one head” event counted above, expressed through an observable.[ref]
Random: Probability, Mathematical Statistics, Stochastic Processes by Kyle Siegrist. Probability Spaces, §2 Events and Random Variables, Random Variables: measurable functions and the inverse-image notation for value events
Head count merges outcomes into fibers and adds their probabilities
. The random variable sends to zero, both and to one, and to two. The inverse image of is the highlighted event of at least one head. Two arrows meeting at one explain its probability . Details.Every event determined by is a union of complete fibers. There are such events, whereas the full power set of has . In particular, the first-head event is not determined by : it contains but excludes , although both give . Choosing an observable makes calculations simpler precisely by forgetting distinctions; the useful choice is one that retains the distinctions the question needs.
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