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Winkler G. — Stochastic Integrals
Winkler G. — Stochastic Integrals



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Название: Stochastic Integrals

Автор: Winkler G.

Язык: en

Рубрика: Математика/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Год издания: 1990

Количество страниц: 336

Добавлена в каталог: 10.12.2005

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID
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Предметный указатель
$(F_t)$-Brownian motion      3.0.2
$(F_t)$-Poisson process      3.0.2
$\sigma$-field of the past      2.1.2
$\sigma$-field, strictly before T      6.4.8
$\sigma$-field, up to T      2.2.3
A.s. regular process      4.4.6
Adapted process      2.1.2
Admissible measure      6.5.1
Almost sure equality      2.0
Backward equation      1.1
Band Pass Filter      1.2(C)
Blumenthal’s-0-l-law      (2.1.4) 9.7.2
Bochner — Khintchine theorem      1.2.(B)
Borel-$\sigma$-field      1.1
Brownian filtration      9.7.3
Brownian motion      1.1
Brownian motion, $(F_t)$-      3.0.2
Brownian motion, complex      9.5.1
Brownian motion, d-dimensional      9.0
Brownian motion, planar      9.7.1
Brownian motion, standard      1.1
Brownian motion, starting at x      9.0
Brownian motion, stopped      9.1.6
Brownian transition function      1.1
Burkholder — Davies — Gundy, inequality      11.2.1
Cameron — Martin formula      10.2.8
Cauchy process      9.2.6
Change of drift      (10.2.5) 10.2.6
Compensator      6.6.4
Complex, Brownian motion      9.5.1
Complex, Ito formula      8.6.5
Complex, mutual variation      8.6.4f
Complex, quadratic variation      8.6.4 f
Conformal martingale      9.5.3f
Convergence theorem for (sub-) martingales      3.2.4
Dambis — Dubins — Schwarz theorem      9.2.3
Desintegration predictable      6.5.5
Differential      7.3.3
Diffusion      12.0.1
Diffusion, matrix      12.0.1
Diffusion, operator      12.0 12.2
Diffusion, process      12.0.1
Dirichlet problem      9.3.3
Dominated convergence for stochastic integrals      8.3.1
Doob — Meyer decomposition      6.6.1
Doob’s $L^2$-inequality      3.1.4 3.2.10
Doob’s for local martingales      5.6.5
Doob’s maximal inequalities      3.1.2
Doob’s upcrossing inequality      3.1.5
Drift vector      12.0.1
Dual predictable projection      6.5.5
Dubins — Schwarz      see “Dambis — Dubins — Schwarz”
Dynkin’s formula      12.1.5 (12.5.2)
Elementary process      4.4.1
Elementary stochastic integra      5.2.1
Elliptic operator      12.2.1f
Entrance time      2.3.3
Equal a.s. (processes)      2.0
Event known up to T      2.2.3
Exponential      8.2.1f
Exponential martingale      8.2
Exponential of a process      8.2.1f
Exponential process      8.2
Extension Principle      5.1.3f
Feynman — Kac formula      (12.3.5) (12.5.3)
Filter      1.2(C)
Filter, band pass      1.2(C)
Filter, differentiation      1.2(C)
Filter, integration      1.2(C)
Filter, low pass      1.2(C)
Filtration      2.1.1
Filtration, natural      2.1.2
Filtration, own      2.1.2
Filtration, right-continuous      2.1.3
Finite variation      5.1.2
Fokker — Planck equation      1.1
Follmer — Doleans measure      6.2.5
Forward equation      1.1
Function of finite variation      5.1.2
Function, regular      3.2.5
Functional differential equation      11.0
Generator of semigroup      12.5.3f
Girsanov — Maruyama theorem      10.2.1
Girsanov’s theorem      10.2.1
Gronwall’s lemma      11.2 f
Harmonic function      9.3.1
Hermite polynomial      9.6.2
Homogeneous transition function      9.4
Increasing process      5.6.1
Increasing process of Brownian motion      5.6.6
Increasing process of the Poissonmartingale      5.6.7
Increasing process, predictable      6.6.4
Indistinguishable      2.0
Infinitesimal mean      12.0
Infinitesimal parameter      12.0
Infinitesimal variance      12.0
Initial value problem (stochastic)      11.1.1
Integral, elementary      5.2.1
Integral, pathwise      5.3.3
Integral, Stieltjes      5.1.3
Integral, Stochastic      5.4.5 6.2.3 6.3.4
Integration by parts      7.3.1f
Interval partition      5.5.5
Interval, stochastic      4.1.1
Ito exponential      8.2
Ito formula      8.1.1
Ito formula with jumps      8.4.1
Ito formula, complex      8.6.5
Ito formula, matrix-valued      8.6.2
Ito formula, multidimensional      8.6.1
Ito process      10.2.6
Jump process      7.3.17f
Kelvin transformation      9.5.8f
Knight’s theorem      9.2.6
l.u.p. convergence      4.3.2
Langevin equation      1.2(B)
Left-continuous process      2.0
Levy’s theorem      9.1.1
Linear filter      1.2(C)
Lipschitz (stochastic) condition      11.1
Lipschitz (stochastic) constant      11.1
Local ($L^2$-)martingale      4.2.1
Local time      8.3.1f
Localizing sequence      4.1.2
Locally      4.1
Locally bounded      4.1.2
Locally finite variation      5.3.1
Locally integrable      6.2.7f
Locally uniformly in probability      4.3.2
Low pass filter      1.2(C)
M/G/1-queue      5.7
Markov property      9.1.11 11.3.2 12.5
Martingale      3.0.1
Martingale, characterization of      3.2.9
Martingale, closed      3.2.3
Martingale, conformal      9.5.2f
Martingale, convergence theorem      3.2.4
Martingale, criteria      4.2.3 5.6.3 9.2.4 10.2.3
Martingale, problem      12.6.1
Maximal inequalities      3.1.2
Maximum principle      12.2.1f
Modification      2.0
Monotone class theorem      6.1.4
Mutual variation      7.3.1
Mutual variation, complex      8.6.4f
Natural filtration      2.1.2
Neighbourhood recurrent      9.3.5f
Nonanticipating      2.1.2f
Novikov’s criterion      10.2.2
Nullset, augmentation      2.1.5
Nullset, elimination      2.1.5 f
Optional $\sigma$-field      6.1.6f
Optional sampling      3.2.8
Ornstein — Uhlenbeck process      1.2.(B)
Orthogonal increments      1.1
Own filtration      2.1.2
Partial integration      7.3.1f
Partition      5.5.5 7.3.3f
Pasta      5.7
Path      2.0
Pathwise, integral      5.3.3
Pathwise, solution      12.4.1
Picard — Lindelof theorem (for SDEs)      11.1.1
Picard’s Theorem      9.5.6f
Planar Brownian motion      9.5.1
Poisson martingale      1.1
Poisson process      1.1
Poisson transition function      1.1
Polish space      1.1
Predictable      6.1.1
Predictable, desintegration      6.5.5
Predictable, increasing process      6.6.4
Predictable, measure      6.5.7f
Predictable, process      6.1.1
Predictable, projection      6.5.3
Predictable, rectangle      6.1.2
Predictable, set      6.1.1
Predictable, stopping theorem      6.4.10
Predictable, stopping time      2.3.5 6.4.1
Process with orthogonal increments      1.2.(B)
Process, elementary      4.4.1
Process, predictable      6.1.1
Process, progressively measurable      2.3.8
Process, separable      2.3.1
Process, stationary      1.2.(B)
Process, weakly-stationary      1.2.(B)
Product measurable      6.3.1
Progressively measurable      2.3.8
Projection, dual predictable      6.5.4
Projection, predictable      6.5.3
Quadratic variation      5.6.1 7.3.1
Quadratic variation, complex      8.6.4f
Recurrence of Brownian motion      9.3.5
Regular function      3.2.5
Riesz representation theorem      1.2
Right-continuous filtration      2.1.3
Schrbdinger equation      12.3
SDE      11.0
Second order process      1.2(C)
Semigroup      12.5.3
Semimartingale      7.1.1
Separability set      2.3.1
Separable process      2.3.1
Signed measure      1.2
Signum      8.3 12.4.3
Sojourn time      12.3.7F
Solution of SDE      11.1.0
Space-time-harmonic      12.1.2f
Special semimartingale      7.2.7
Spectral density      1.2(C)
Spectral measure      1.2.(C)
Spectral representation      1.2.(C)
Standard Brownian motion      1.1
Stationary (weakly)      1.2.8
Stationary increment      1.2.8
Step function      4.4.1
Step function, left-continuous      4.4.1
Stieltjes integral      5.1.3
Stochastic differential      7.3.3
Stochastic differential equation      11.0
Stochastic differential equation, solution of      11.1
Stochastic integral      5.2.1 5.3.6 5.4.5 6.1.5 6.2.3 6.3.4 7.1.5
Stochastic integral for semimartingales      7.1.5
Stochastic integral, elementary      5.2.1
Stochastic interval      4.1.1
Stochastic process      2.0
Stopped Brownian motion      9.1.6
Stopping theorem, continuous      3.2.7
Stopping theorem, discrete      3.1.2
Stopping time      2.2.1
Stopping time, predictable      2.3.5 6.4.1
Stopping time, strict      2.2.1
Stopping time, wide sense      2.2.1f
Stratonovich — Fisk, differential      8.5.1
Stratonovich — Fisk, integral      8.5.1
Strict stopping time      2.2.1
Strong Markov property      9.1.11 11.3.2 12.5
Submartingale      3.0.1
Submartingale, closed      3.2.3
Submartingale, convergence theorem      3.2.4
Supermartingale      3.0.1
Symmetric multiplication      8.5.3
System function      1.2(C)
T-past      2.2.3
Tanaka’s differential equation      12.4.3
Test-function      12.0.1
Time change      9.2
Total variation      5.1.2
Transience of Brownian      motion 9.3.5
Uniformly elliptic      12.2.1f
Uniformly integrable      3.2.1f
Uniqueness in distribution      12.4.1
Uniqueness theorem for SDEs      11.1.1 12.1.8
Uniqueness, pathwise      12.4.1
Uniqueness, weak      12.4.1
Upcrossing      3.1.4f
Upcrossing inequality      3.1.5
Usual conditions      2.1.5 12.5
Variation      5.1.2
Variation, locally finite functions      5.1.2
Variation, locally finite processes      5.3.1
Variation, mutual      7.3.1
Variation, quadratic      7.6.2f
Variation, total (functions)      5.1.2
Version      2.0
Version of the pathwise integral      5.3.3 5.3.6
Walds identity      5.6.6
Weak maximum principle      12.2.1f
Weak solution      12.4.?
Weak uniqueness      12.4.?
Weight function      1.2(C)
White noise      1.2(C)
Wide sense stopping time      2.2.1f
Wiener, chaos      9.6.8 f
Wiener, integral      1.2(C)
Wiener, measure      1.1
Wiener, space      1.1
Yamada — Watanabe theorem      12.4.4
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