It is now clear that many problems that could only be addressed using ad hoc methods, because of their complexity, can now be solved and these solutions can be applied to almost all areas of data and signal processing. Bayesian methods have been popular for decades. However, various ...
We demonstrate the usefulness of the ensemble solutions by computing empirical pdfs of several informative statistical parameters, the calculation of which would be difficult by traditional means. 展开 关键词: Curve fitting filter estimation implicit inverse problem Markov chain Monte Carlo Metropolis–...
The four parallel computing solutions are: (1) multicore processor parallel computing (MP), (2) parallel computing by GPU-accelerated nearest neighbor searching (GNNS), (3) MP with GPU-accelerated nearest neighbor searching (MP-GNNS), and (4) parallel computing by GPU-accelerated approximation ...
This is primarily because of the emergence of Markov chain Monte Carlo (MCMC) methods. While MCMC provides a convenient way to draw inference from complicated statistical models, there are many, perhaps underappreciated, problems associated with the MCMC analysis of mixtures. The problems are mainly...
Controlled Markov chains with constraints 来自 Springer 喜欢 0 阅读量: 23 作者: VS Borkar 摘要: We consider the ergodic control of a Markov chain on a countable state space with a compact action space in presence of finitely many (say, m ) ergodic constraints. Under a condition on the ...
We study (backward) stochastic differential equations with noise coming from a finite state Markov chain. We show that, for the solutions of these equations to be `Markovian', in the sense that they are deterministic functions of the state of the underlying chain, the integrand must be of a...
To enable such a feedback, we propose to extend the state of the Markov chain with the information about the outcome of the last component execution; this extension is simple but will prove to be effective. In particular, we suggest to distinguish executions that improved the solution quality,...
In this chapter, we will define some of the canonical control problems for the Markov chain models which will be used in the sequel as “approximating processes.” The cost functions will be defined. The functional equations which are satisfied by these cost functions for fixed controls, as ...
Consequently, a combination of both approaches substantially increases the quality of the solutions. We present a thorough analysis of the proposed methods in synthetic MRF problems by controlling the hardness of the problems. We also demonstrate experimental results for the photomontage problem which is...
Moreover, in the literature, to prove the convergence using Markov chain approximation methods for control problems involving cost functions with stopping (even for uncontrolled diffusion without switching), an added assumption was used to avoid the so-called tangency problem. In this paper, by ...