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Friday, 16 May 2025

Stochastic Meaning Theory 5 Language as Brown Motion For ZHANG Taiyan 2

 Stochastic Meaning Theory 5

 

Language as Brown Motion

For ZHANG Taiyan 2

 

TANAKA Akio

 

[A]

1

Abstractive space     Ω

σ additive family that consists of subset of Ω     F

Measure that is defined over F     P

P satisfies P (Ω ) = 1. 

P      probability measure over ( Ω, F )

Ω      sample space     

( Ω, F , P )     Probability space

Element of Ω     sample ω

Element of F     event A

Probability that event A occurs     probability P ( A )

Real number valued Borel measurable function over Ω     random variable X = X ( ω )

Random variable is integrable.

Mean (Expectation) of X     E[X] = ∫Ω X ( ω ) P ( dω )

2

Measurable space     ( S, S )

X : ( Ω, F ) → ( S, S )

X is measurable.

X      S-value random variable.

Random variable     X1, …, Xd

X : = (X1, …, Xd )     Rd-value random variable

3

Rd-value random variable     X

E[X i 2] < ∞

E[(X - E[X])2]     variance

4

S-value random variable.     X

PX : = P ( X ∈A ), A∈S     distribution

5

Real number space     R

Borel set family over R    B ( R )

Probability measure over ( R, B ( R ) )     μ

6

Rd-value random variable     X

ψX (ξ ) : = E[eiξ・X], ξ∈Rd     characteristic function

7

Lebesgue measure     dx

Mean     m∈R

Variance     v >0

Measure over R     μ ( dx ) = e -(x-m)2 / 2v dx /     Gauss distribution ( normal distribution)

8

(2p – 1 ) !! : = (2p – 1 ) ・(2p – 3 ) … 3・1

E[X2p] = (2p – 1 ) !! v p     moment of X

9

Event     A, B∈F

When (A∩B) = P(A) P(B), A and B are independent each other.

10

Integrable and independent random variable     X, Y

Product XY     integrable

E[XY] = E[X]E[Y]

11

Time     t

t ∈[0, ∞)

Family of Rd-value random variable ≥    X = ( Xt ) t ≥ 0     d-dimensional stochastic process

∀ω∈Ω

When Xt (ω) is continuous as function of t., d-dimensional stochastic process is called to be continuous.

12

σ additive family     Ft

Ft  ⊂F

0 ≤ s ≤ t

F s  ⊂Ft

(Ft  ) = (Ft  ) t ≥ 0   increase information system

13

d-dimensional stochastic process     X = ( Xt ) t ≥ 0    

∀t ≥ 0

Xt : Ω → Rd  is Ft – measurable.

X = ( Xt ) t ≥ 0 is (Ft ) – adapted.

14

Mapping ( t, ω) ∈([0, ∞)×Ω, B([0, ∞)]×F) ↦ Xt ( ω) ∈( Rd, B ( Rd ) )

When the mapping is measurable, X = ( Xt ) t ≥ 0  is called to be measurable.

15

X = ( Xt )

Ft0 = Ft0,X : = σ ( XS ; s≤t )

16

Probability space      ( Ω, F , P )    

Stochastic process defined over  ( Ω, F , P )      (Bt)t ≥ 0 = (Bt(ω)) t ≥ 0

(Bt)t ≥ 0 that satisfies the next, it is called Brownian motion.

(i) P ( B0 = 0  ) = 1

(ii) For ∀ω∈Ω, Bt (ω) is continuous on t.

(iii) For 0 = t0<∀t1<…<tn, ∀n∈N, {Bti-Bti-1} satisfies the next.

a) {Bti-Bti-1} are independent each other.

b) {Bti-Bti-1} are followed by mean 0 and variance ti-ti-1 of Gauss distribution.

17

(Existence theorem)

Over adequate probability space, there exists Brownian motion.

18

Ω = W0

F = B ( W0 )

Brownian motion has the next.

(i)Bt ( w ) = Wt

(ii) w = ( wt ) t ≥0 ∈W 0

Measure over ( W0, B ( W0 ) )      P

P is called Wiener measure.

19

d-dimensional Brownian motion     B = ( Bt ) t ≥ 0

d×d orthogonal matrix     A

ABt     d-dimensional Brownian motion

Sphere     S : = δ B (0, r),  B (0, r) = {|x| ≤ r }

Hitting time     σS (ω) : = inf{t >0; Bt ∈S }

Hitting place    BσS (ω)

Distribution of BσS (ω)      uniform stochastic measure

20

d-dimensional Brownian motion     B = ( Bt ) t ≥ 0

x∈Rd

Brownian motion from x     ( x + Bt ) t ≥ 0

W  d = B ( W d )

Space     (W d, W d )

Distribution over  (W d, W d )     Px

Mean on Px     Ex [ ・ ]

Probability space     (W d, W d , Px )

Stochastic process over (W d, W d , Px )    Bt ( w ), w∈W d ; Bt ( w ) = wt

Sub σ additive family of W d     Ft0 =σ (Bs ; s≤t ) , Ft = Ft0 ⋁ N, t≥0 ; N : = {N∈W d ; Px (N) = 0, ∀x ∈Rd }

Ft* = Ft+ : = ∩s>t Fs

Shift operator over W d     θs : W d → W d , s≥0 ; (θs (w) ) t : = wt+s

Bt ∘  θs  = Bt+s

21

(Markov property)

∀x∈Rd

∀s≥0

∀Y = Y (w) : W d –measurable bounded function over W d

Ex[Y∘θs ・1A] = Ex[EBs(w)[Y]∘θs ・1A] , ∀A∈F s

By conditional mean

Ex[Y∘θs | Fs] (w) = EBs(w)[Y

Px-a.s.w

22

(Blumenthal’s 0-1 law)

When A∈F0 ( = F0* ), Px (A) = 0 or 1

23

Random variant of 1-dimensional Brownian motion starting from the origin     B

σ (0,∞) : = inf {t >0; Bt∈(0,∞) }

A = {σ(0,∞) = 0 }

A ∈F0*

P (σ(0,∞) = 0 ) = 0 or 1

t↓0

P (σ(0,∞) = 0 ) = 1

From symmetry of Brownian motion Bt = -Bt

 

[B]

Language that has Brownian motion     LB

LB has actual language and imaginary language.

 

[References]

Mirror Theory For the Structure of Prayer / Dedicated to the Memory of CHINO Eiichi / Tokyo June 5, 2004

Mirror Language / Tokyo June 10, 2004

Guarantee of Language / For LÉVI-STRAUSS Claude / Tokyo June 12, 2004

Actual Language and Imaginary Language / To LÉVI-STRAUSS Claude / Tokyo September 23, 2004

 

 

To be continued

Tokyo August 12, 2008

Sekinan Research Field of Language

www.sekinan.org

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