Backward stochastic dynamics on a filtered probability space
نویسندگان
چکیده
منابع مشابه
A Probability Space based on Interval Random Variables
This paper considers an extension of probability space based on interval random variables. In this approach, first, a notion of interval random variable is introduced. Then, based on a family of continuous distribution functions with interval parameters, a concept of probability space of an interval random variable is proposed. Then, the mean and variance of an interval random variable are intr...
متن کاملIs There a Predictable Criterion for Mutual Singularity of Two Probability Measures on a Filtered Space?
The theme of providing predictable criteria for absolute continuity and for mutual singularity of two density processes on a ltered probability space is extensively studied, e.g., in the monograph by J. Jacod and A. N. Shiryaev [JS]. While the issue of absolute continuity is settled there in full generality, for the issue of mutual singularity one technical di culty remained open ([JS], p210): ...
متن کاملStochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex
In this paper we investigate the use of Langevin Monte Carlo methods on the probability simplex and propose a new method, Stochastic gradient Riemannian Langevin dynamics, which is simple to implement and can be applied to large scale data. We apply this method to latent Dirichlet allocation in an online minibatch setting, and demonstrate that it achieves substantial performance improvements ov...
متن کاملBackward Induced Probability Models
This paper describes how to specify probability models for data analysis via a backward induction procedure. The new approach yields coherent, priorfree uncertainty assessment. The backward induction approach is first demonstrated on two familiar models — the Bernoulli distribution and the Gaussian distribution — to compare the resulting specifications to their standard counterparts arising as ...
متن کاملEffects of Probability Function on the Performance of Stochastic Programming
Stochastic programming is a valuable optimization tool where used when some or all of the design parameters of an optimization problem are defined by stochastic variables rather than by deterministic quantities. Depending on the nature of equations involved in the problem, a stochastic optimization problem is called a stochastic linear or nonlinear programming problem. In this paper,a stochasti...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Annals of Probability
سال: 2011
ISSN: 0091-1798
DOI: 10.1214/10-aop588