On the computation of density functions of parameters in stochastic systems.
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On the computation of density functions of parameters in stochastic systems. by Boris Segerstaahl

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Published by The Finnish Academy of Technical Sciences in Helsinki .
Written in English


Book details:

Edition Notes

Thesis for the degree of Doctor of Technology.

SeriesActa Polytechnica Scandinavica : Mathematics and Computing Machinery Series -- No. 18
The Physical Object
Pagination42 p.
Number of Pages42
ID Numbers
Open LibraryOL20633863M

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Applied Stochastic Processes is a collection of papers dealing with stochastic processes, stochastic equations, and their applications in many fields of science. One paper discusses stochastic systems involving randomness in the system itself that can be a large dynamical multi-input, multi-output system. Stochastic computation graphs (SCGs) are directed acyclic graphs in which nodes are determinsitic or stochastic functions, and edges indicate functional dependencies [Schulman et al., a]. Stochastic Computation Naresh R. Shanbhag, Rami A. Abdallah, Rakesh Kumar, and Douglas Jones in the design of stochastic computing systems. 2. RELEVANT WORK Von Neumann [9] was the rst to address the problem of reliable computation in presence of unreliable Size: KB. Fig. 2. Multiplexer used as a scaled stochastic adder. Nepal et al. [] and Vigoda []. The terms “stochastic numbers” and “stochastic arithmetic” appear in Alt et al. [] which, however, is concerned with numerical errors in conventional binary computation. In this article, we attempt to survey and critique SC from a modern.

circularity prevent the numerical solution of the Fokker-Planck equation for stochastic delay systems. Our method bypasses these issues and o ers one of the rst deterministic algorithms to compute the density of a nonlinear stochastic delay system. Keywords: stochastic di erential equations, time delay, nonlinear systems, numerical analysis 1. Abstract. The shape control of probability density function (PDF) of the system state is an important topic in stochastic systems. In this paper, we propose a control technique for PDF shape of the state variable in nonlinear stochastic by: 2. It should be noticed that the CRLB may only be computed if the probability density function of the observations is known. At first sight, this seems to be a problem since the true parameters of the probability density function are unknown. Nevertheless, even if the CRLB is a function of the unknown parameters, it remains an extremely useful tool. NSF/CBMS Regional Conference in the Mathematical Sciences (Supported by NSF) Nonlocal Dynamics: Theory, Computation and Applications, Jinqiao Duan & Xiaofan Li, NSF NSF-DMS "Theoretical and Numerical Studies of Nonlocal Equations Derived from Stochastic Differential Equations with Levy Noises".

@article{osti_, title = {Direct computation of stochastic flow in reservoirs with uncertain parameters}, author = {Dainton, M P and Nichols, N K and Goldwater, M H}, abstractNote = {A direct method is presented for determining the uncertainty in reservoir pressure, flow, and net present value (NPV) using the time-dependent, one phase, two- or three-dimensional equations of flow through a. This book is a great reference book, and if you are patient, it is also a very good self-study book in the field of stochastic approximation. The book is written in Cited by: A key step in mathematical modelling is to estimate model parameters in order to realize experimental observations. However, it is difficult to derive the analytical density functions in the Bayesian methods for these mathematical models. During the last decade, approximate Bayesian computation (ABC) has been developed as a major method for the Author: Yuqin Ke, Tianhai Tian.   The book is based on the author's lectures on topics on computational finance for senior and graduate students, delivered in USA (Princeton University and EMU), Taiwan and Kuwait. The prerequisites are an introductory course in stochastic calculus, as well as the usual calculus sequence.