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G-expectation : ウィキペディア英語版
G-expectation
In probability theory, the g-expectation is a nonlinear expectation based on a backwards stochastic differential equation (BSDE) originally developed by Shige Peng.
== Definition ==
Given a probability space (\Omega,\mathcal,\mathbb) with (W_t)_ is a (''d''-dimensional) Wiener process (on that space). Given the filtration generated by (W_t), i.e. \mathcal_t = \sigma(W_s: s \in ()), let X be \mathcal_T measurable. Consider the BSDE given by:
: \begindY_t &= g(t,Y_t,Z_t) \, dt - Z_t \, dW_t\\ Y_T &= X\end
Then the g-expectation for X is given by \mathbb^g() := Y_0. Note that if X is an ''m''-dimensional vector, then Y_t (for each time t) is an ''m''-dimensional vector and Z_t is an m \times d matrix.
In fact the conditional expectation is given by \mathbb^g(\mid \mathcal_t ) := Y_t and much like the formal definition for conditional expectation it follows that \mathbb^g for any A \in \mathcal_t (and the 1 function is the indicator function).〔

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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