probability for a first-order language ken presting university of north carolina at chapel hill
TRANSCRIPT
Probability for a First-Order Language
Ken Presting
University of North Carolina
at Chapel Hill
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A qualified homomorphism
• If A, B disjoint
P(A B) = P(A) + P(B)∪
• If A, B independent
P(A ∩ B) = P(A) · P(B)
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Quotient by a Subalgebra
• Let x, y, ~x, ~y be pairwise independent• Direct product of factors = {x, ~x} x {y, ~y}• Probability is area of rectangles in unit square
x ~x
y
~y
~x·yx·y
x·~y ~x·~y
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Probability on Extensions
• A predicate is true-of an individual– Set of individuals is the extension– Measure of that set is probability
• A generalization is true-in a domain– Set of domains is the extension– Measure of that set is the probability
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Quotient by an Ideal
• If Fx is a predicate in L, then every sample is a disjoint union, split by [Fx] and [~Fx]
• Sample space Σ is a direct sum of principal ideals,
Σ = <Fx> ⊕ <~Fx> = [ xFx] ∀ ⊕ [ x~Fx]∀• Conditional [ x(Fx∀ Gx)] = [ x(Fx&Gx)] ∀ ⊕ [ x~Fx]∀
[ x(Fx∀ Gx)]
[ x~Fx]∀
[ xFx]∀
[ x(Fx&Gx)]∀
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Definitions
• The Domain space - <Ω, Σ, P0> – Ω is a domain of interpretation for L (with N members)– Σ is generated by predicates of L
– For any S in Σ, we set P0(S) = |S|/N
• The Sample Space - <Σ, Ψ, P> – Σ is the field of subsets from the space above– Ψ is generated by closed sentences of L– For any C in Ψ, we set P(C) = |C|/2N
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Sentences and Extensions
• Extensions of Formulas– (only one free variable)– [Fx] = { s in Ω | ‘Fs’ is true in L }
• Extensions of Sentences– [x(Fx)] = { S in Σ | ‘x(Fx)’ is “true in S” } – = { S in Σ | S is a subset of [Fx] }
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Theorem
• Let L be a first-order language
• Probability P and P0 as above
• If ‘Fx’, ‘Gx’ are open formulas of L, then
P[x(Fx Gx)] = P[x(Gx) | x(Fx)].
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Proof
• Define values for predicate extensionsNf = |[Fx]|
Ng = |[Gx]|
Nfg = |[Fx & Gx]|
• Calculate sentence extensions|[x(Fx)]| = 2Nf
|[x(Gx)]| = 2Ng
|[x(Fx & Gx)]| = 2Nfg
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Conditional Probability
• P[x(Gx) | x(Fx)] = P[x(Fx & Gx)]
P[x(Fx)]
=
=fg
f
N
N
2
2
fg
f
N N
N N
2 2
2 2
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Probability of the Conditional
• Extension of open material conditional|[Fx Gx]| = |[~Fx] v [Fx & Gx]|
= (N-Nf) + Nfg
• Extension of its generalization|[x(Fx Gx)]| =
=
• Probability
f fg((N - N ) + N )2fgf
N-NN(2 )(2 )(2 )
fgf
N-NN
N
(2 )(2 )(2 )P x(Fx Gx)
2
fg
f
N
N
2
2
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Relations on a Domain
• Domain is an arbitrary set, Ω
• Relations are subsets of Ωn
• All examples used today take Ωn as ordered tuples of natural numbers,
Ωn = {(ai)1≤i≤n | ai N }
• All definitions and proofs today can extend to arbitrary domains, indexed by ordinals
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Hyperplanes and Lines
• Take an n-dimensional Cartesian product, Ωn, as an abstract coordinate space.
• Then an n-1 dimensional subspace, Ωn-1, is an abstract hyperplane in Ωn.
• For each point (a1,…,an-1) in the hyperplane Ωn-
1, there is an abstract “perpendicular line,” Ω x {(a1,…,an-1)}
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Decomposition of a Relation
Hyperplane, Perpendicular Line, Graph and Slice
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Slices of the Graph
• Let F(x1,…,xn) be an n-ary relation• Let the plain symbol F denote its graph:
F = {(x1,…,xn)| F(x1,…,xn)}
• Let a1,…,an-1 be n-1 elements of Ω
• Then for each variable xi there is a setFxi|a1,…,an-1 = { ωΩ | F(a1,…,ai-1,ω,ai,…, an-1}
• This set is the xi’s which satisfy F(…xi…) when all the other variables are fixed
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The Matrix of Slices
• Every n-ary relation defines n set-valued functions on n-1 variables:
Fxi(v1,…,vn-1) = { ωΩ | F(v1,…,vi-1,ω,vi,…,vn-1) }
• The n-tuple of these functions is called the “matrix of slices” of the relation F
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Example: x2 < x3
Index Value of x1 Value of x2 Value of x3 Value of x4
0,0,0 Ω Ø {1,2,3,…} Ω
0,0,1 Ω Ø {1,2,3,…} Ω
0,0, … Ω Ø {1,2,3,…} Ω
0,1,0 Ω {0} {2,3,4,…} Ω
0,1,1 Ω {0} {2,3,4,…} Ω
… Ω … … Ω
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Boolean Operations on Matrices
• Matrices treated as vectors– direct product of Boolean algebras– Component-wise conjunction, disjunction, etc.
• Matrix rows are indexed by n-1 tuples from Ωn
• Matrix columns are indexed by variables in the relation
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Cylindrical Algebra Operations
• Diagonal Elements– Images of identity relations: x = y– Operate by logical conjunction with operand relation
• Cylindrifications– Binding a variable with existential quantifier
• Substitutions– Exchange of variables in relational expression
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The Diagonal Relations
• Matrix images of an identity relation, xi = xj
• Example. In four dimensions, x2 = x3 maps to:
Index Value of x1
Value of x2
Value of x3
Value of x4
0,0,0 Ω {0} {0} Ω
0,0,1 Ω {0} {0} Ω
0,0, … Ω {0} {0} Ω
0,1,0 Ω {1} {1} Ω
0,1,1 Ω {1} {1} Ω
… Ω … … Ω
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Cylindrical Identity Elements
• 1 is the matrix with all components Ω, i.e. the image of a universal relation such as xi=xi
• 0 is the matrix with all components Ø, i.e. the image of the empty relation
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Diagonal Operations
• Boolean conjunction of relation matrix with diagonal relation matrix
• Reduces number of free variables in expression, ‘x + y > z’ & ‘x = y’
• Constructs higher-order relations from low order predicates
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Instantiation
• Take an n-ary relation, F = F(x1,…,xn)
• Fix xi = a, that is, consider the n-1-ary relation F|xi=a = F(x1,…,xi-1,a,xi+1,…,xn)
• Each column in the matrix of F|xi=a is:
Fxj|xi=a(v1,…,vn-2) =
F(v1,…,vj-1,xj,vj,…,vi-1,a,vi+1,…,vn)
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Cylindrification as Union
• Cylindrification affects all slices in every non-maximal column
• Each slice in F|xi is a union of slices from
instantiations:Fxj|xi
(v1,…,vn-2) = U Fxj|xi=a(v1,…,vn-2)
aΩ
• Component-wise operation