Probability theory and stochastic processes Foundations: probability spaces and events Theorem
Law of total probability
Statement
Let be a probability space. Let be a finite or countable family of pairwise disjoint events with and for each . Then for each event ,
Remarks
The events form a partition of into events of positive probability. The proof uses , countable additivity and the multiplication rule. The case of two events and is the law of total probability for an event and its complement. Example: urn has red balls of and urn has red balls of . Choose an urn with a fair coin and draw one ball. The ball is red with probability .
Reading path
To understand this page from the ground up, read these 38 pages in order. The foundations come first.
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- no evidence level
- Construction of the reals from Cauchy sequences concept
- Element definition
- Integers object
- Ordered field definition
- Proposition concept
- Random experiment concept
- Rational numbers object
- Set definition
- Ring of integers statement
- Conjunction definition
- Disjunction definition
- Implication definition
- Predicate definition
- Empty set object
- Family of sets definition
- Intersection definition
- Union definition
- Sample space definition
- The rationals form a field statement
- Existential quantifier definition
- Universal quantifier definition
- Disjoint sets definition
- Subset definition
- Union of a family of sets definition
- The rationals form an ordered field statement
- Universal set definition
- Partition of a set definition
- Power set definition
- Order on the reals definition
- Complement definition
- Interval definition
- Sigma-algebra definition
- Event definition
- Probability axioms concept
- Probability measure definition
- Probability space definition
- Conditional probability definition
- Law of total probability statement
Where this leads
25 pages build on this one.
- Prior probability
- Chapman–Kolmogorov equation
- Law of total variance
- Law of total expectation
- Base rate fallacy
- Bayesian inference
- Posterior probability
- Bayes' theorem
- Monty Hall problem
- Law of total probability for an event and its complement
- Gambler's ruin
- Absorbing Markov chain
- Bernstein–von Mises theorem
- Empirical Bayes method
- Bayes factor
- Lindley's paradox
- Bayes estimator
- Continuous-time Markov chain
- Bayes risk
- Credible interval
- Jeffreys prior
- Bayesian hierarchical model
- Conjugate prior
- Shrinkage estimator
- Particle filter
Further reading
About this page
| Tag | 0E5G5FMT2R |
|---|---|
| URI | m2://0E5G5FMT2R |
| Aliases | rule-of-elimination |
| Kind | statement, theorem |
| Writing | W1 stated |
| MSC | 60A05 |
| Status | This copy of the site has no build data. Run m2 status 0E5G5FMT2R. |
| Uses | Conditional probability, Partition of a set |
| Same as |
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What this page needs for W2
- edge about out: the concepts that the statement is about (0 of 1)
- edge proves in: at least one proof node (0 of 1)
- edge instance_of or illustrates in: one example that shows the statement at work (0 of 1)
- explanation L2 (0 of 1)
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