Math 2.0

Numerical analysis Definition

Coupling from the past

Builds on Markov chainTransition matrixStationary distributionContinuous uniform distributionCoupling

Definition

Let SS be a finite set and let PP be the transition matrix of an irreducible and aperiodic Markov chain on SS. Let π\pi be its stationary distribution. Let ϕ ⁣:S×[0,1]→S\phi \colon S \times [0, 1] \to S be an update function: if UU is uniform on [0,1][0, 1], then P(ϕ(x,U)=y)=P(x,y)P(\phi(x, U) = y) = P(x, y) for all x,y∈Sx, y \in S. Let U−1,U−2,…U_{-1}, U_{-2}, \ldots be independent and uniform on [0,1][0, 1]. For t≥1t \ge 1 define the map F−t ⁣:S→SF_{-t} \colon S \to S by F−t=ϕ−1∘ϕ−2∘⋯∘ϕ−t,ϕ−k(x)=ϕ(x,U−k).F_{-t} = \phi_{-1} \circ \phi_{-2} \circ \cdots \circ \phi_{-t}, \quad \phi_{-k}(x) = \phi(x, U_{-k}). Coupling from the past is the algorithm that computes F−tF_{-t} for t=1,2,4,8,…t = 1, 2, 4, 8, \ldots, always with the same U−kU_{-k}. It stops at the first such tt for which F−tF_{-t} is constant, and it outputs the common value F−t(x)F_{-t}(x).

Remarks

If τ=min⁡{t:F−t is constant}\tau = \min\set{t : F_{-t} \text{ is constant}} is finite almost surely, the output has law exactly π\pi. Indeed, F−t=F−τ∘(ϕ−τ−1∘⋯∘ϕ−t)F_{-t} = F_{-\tau} \circ (\phi_{-\tau-1} \circ \cdots \circ \phi_{-t}) has the same constant value for all t≥τt \ge \tau, while F−t(x)F_{-t}(x) has law Pt(x,⋅)P^t(x, \cdot), which tends to π\pi. Example: take a chain that jumps to a fresh sample of π\pi at each step, with an update ϕ(x,u)\phi(x, u) that does not depend on xx. Then F−1F_{-1} is constant, and the output ϕ(x,U−1)\phi(x, U_{-1}) has law π\pi. Non-example: running the coupled copies forward from time 00 and outputting their value at the first meeting time is biased. Take S={0,1}S = \set{0, 1}, ϕ(0,u)=0\phi(0, u) = 0 for u<1/2u < 1/2, ϕ(0,u)=1\phi(0, u) = 1 otherwise, and ϕ(1,u)=0\phi(1, u) = 0. Then the forward copies always meet at 00, while π=(2/3,1/3)\pi = (2/3, 1/3). If SS has a partial order with a top and a bottom element and ϕ(⋅,u)\phi(\cdot, u) is monotone, it suffices to follow the top and the bottom states.

Reading path

To understand this page from the ground up, read these 55 pages in order. The foundations come first.

  1. Construction of the reals from Cauchy sequences concept
  2. Continuous uniform distribution concept
  3. Coupling concept
  4. Element definition
  5. Integers object
  6. Ordered field definition
  7. Proposition concept
  8. Random experiment concept
  9. Rational numbers object
  10. Real numbers object
  11. Stationary distribution concept
  12. Transition matrix concept
  13. Set definition
  14. Ring of integers statement
  15. Disjunction definition
  16. Implication definition
  17. Predicate definition
  18. Ordered pair definition
  19. Union definition
  20. Sample space definition
  21. Existential quantifier definition
  22. Universal quantifier definition
  23. The rationals form a field statement
  24. Cartesian product definition
  25. Subset definition
  26. Uniqueness quantifier definition
  27. The rationals form an ordered field statement
  28. Binary relation definition
  29. Universal set definition
  30. Power set definition
  31. Order on the reals definition
  32. Function definition
  33. Complement definition
  34. Interval definition
  35. Injective function definition
  36. Preimage of a set definition
  37. Sigma-algebra definition
  38. Countable set definition
  39. Event definition
  40. Borel sigma-algebra definition
  41. Probability axioms concept
  42. Probability measure definition
  43. Probability space definition
  44. Conditional probability definition
  45. Random variable definition
  46. Independence of two events definition
  47. Discrete random variable definition
  48. Stochastic process concept
  49. Random variable with a density definition
  50. Conditional independence definition
  51. Expected value definition
  52. Conditional expectation definition
  53. Markov property definition
  54. Markov chain definition
  55. Coupling from the past definition

Further reading

  1. Web https://en.wikipedia.org/wiki/Coupling_from_the_past
About this page
TagCA6SVPFR3G
URIm2://CA6SVPFR3G
Kinddefinition
WritingW0 stub
MSC65C05, 60J10, 60J22
DefinesCoupling from the past
UsesMarkov chain, Transition matrix, Stationary distribution, Continuous uniform distribution, Coupling
Changed2 November 2026

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