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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 , )     Probability space

Element of Ω     sample ω

Element of F     event A

Probability that event A occurs     probability P ( )

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

Random variable is integrable.

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

2

Measurable space     ( S)

X : Ω, F )  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 ), AS     distribution

5

Real number space     R

Borel set family over R    R )

Probability measure over ( RR ) )     μ

6

Rd-value random variable     X

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

7

Lebesgue measure     dx

Mean     mR

Variance     v >0

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

8

(2p – 1 ) !! : = (2p – 1 ) (2p – 3 ) … 31

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

9

Event     ABF

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

10

Integrable and independent random variable     XY

Product XY     integrable

E[XY] = E[X]E[Y]

11

Time     t

[0, ∞)

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

ωΩ

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

12

σ additive family     Ft

F F

 s  t

F s  Ft

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

13

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

t ≥ 0

XΩ  Rd  is F– measurable.

X = ( Xt ) t  0 is (F) – adapted.

14

Mapping ( tω([0, ∞)×ΩB([0, ∞)]×F Xt ( ωRdRd ) )

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

15

X = ( Xt )

FtFt0,X : = σ XS st )

16

Probability space      ( Ω, F , )    

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

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

(i) P ( B0 = 0  ) = 1

(ii) For ωΩBt (ω) is continuous on t.

(iii) For 0 = t0<t1<…<tnnN, {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 ) = Wt

(ii) = ( wt ) 0 0

Measure over ( W0, B ( W) )      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; BS }

Hitting place    BσS (ω)

Distribution of BσS (ω)      uniform stochastic measure

20

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

xRd

Brownian motion from x     ( x + Bt ) t  0

 d = B ( W d )

Space     (dd )

Distribution over  (dd )     Px

Mean on Px     Ex [  ]

Probability space     (dd , P)

Stochastic process over (dd , P)    Bt ( w ), wBt ( w ) = wt

Sub σ additive family of d     Ft=σ (Bs ; s) , Ft Ft Nt≥0 ; N : = {Nd ; Px (N) = 0, Rd }

Ft* = Ft+ : = s>Fs

Shift operator over d     θs : → s0 ; (θs (w) ) t : = wt+s

Bt   θ = Bt+s

21

(Markov property)

xRd

s0

Y (w) : d –measurable bounded function over d

Ex[Yθ1A] = Ex[EBs(w)[Y]θ1A] , AF s

By conditional mean

Ex[YθFs] (w) = EBs(w)[Y

Px-a.s.w

22

(Blumenthal’s 0-1 law)

When AF0 ( = 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,) }

= {σ(0,= 0 }

F0*

(σ(0,= 0 ) = 0 or 1

t0

P (σ(0,= 0 ) = 1

From symmetry of Brownian motion Bt = -Bt

 

[B]

Language that has Brownian motion     LB

Lhas 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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