Gradient-based stochastic estimation of the density matrix

Stochastic Information Gradient Algorithm Based on Maximum Entropy Density Estimation

Stochastic Gradient Estimation

We consider the problem of efficiently estimating gradients from stochastic simulation. Although the primary motivation is their use in simulation optimization, the resulting estimators can also be useful in other ways, e.g., sensitivity analysis. The main approaches described are finite differences (including simultaneous perturbations), perturbation analysis, the likelihood ratio/score functi...

Wavelet Based Estimation of the Derivatives of a Density for a Discrete-Time Stochastic Process: Lp-Losses

We propose a method of estimation of the derivatives of probability density based on wavelets methods for a sequence of random variables with a common one-dimensional probability density function and obtain an upper bound on Lp-losses for such estimators. We suppose that the process is strongly mixing and we show that the rate of convergence essentially depends on the behavior of a special quad...

the effect of traffic density on the accident externality from driving the case study of tehran

در این پژوهش به بررسی اثر افزایش ترافیک بر روی تعداد تصادفات پرداخته شده است. به این منظور 30 تقاطع در شهر تهران بطور تصادفی انتخاب گردید و تعداد تصادفات ماهیانه در این تقاطعات در طول سالهای 89-90 از سازمان کنترل ترافیک شهر تهران استخراج گردید و با استفاده از مدل داده های تابلویی و نرم افزار eviews مدل خطی و درجه دوم تخمین زده شد و در نهایت این نتیجه حاصل شد که تقاطعات پر ترافیک تر تعداد تصادفا...

Gradient-Domain Photon Density Estimation

The most common solutions to the light transport problem rely on either Monte Carlo (MC) integration or density estimation methods, such as uni& bi-directional path tracing or photon mapping. Recent gradient-domain extensions of MC approaches show great promise; here, gradients of the final image are estimated numerically (instead of the image intensities themselves) with coherent paths generat...