Semimartingale with smooth density — The problem of "nodes"

1987 ◽  
pp. 356-359
Author(s):  
Zheng Weian
Keyword(s):  
Smooth Density

scholarly journals Smooth density and its short time estimate for jump process determined by SDE

2018 ◽  
Vol 128 (9) ◽  
pp. 3181-3219 ◽  
Author(s):  
Yasushi Ishikawa ◽  
Hiroshi Kunita ◽  
Masaaki Tsuchiya
Keyword(s):  
Jump Process ◽  
Time Estimate ◽  
Short Time ◽  
Smooth Density

Hitting Time Distributions When Σ xk/dk Has a Smooth Density

1979 ◽  
Vol 22 (3) ◽  
pp. 293-298
Author(s):  
Dudley Paul Johnson
Keyword(s):  
Stochastic Processes ◽  
Lebesgue Measure ◽  
Hitting Time ◽  
Smooth Density

AbstractIn this paper we construct the hitting time distributions for stochastic processes Xk, taking on values amongst the integers 0, 1, …, d -1 for which has a smooth polynomial density with respect to the Lebesgue measure on [0,1].

The chemistry in clumpy AGB outflows

2018 ◽  
Vol 14 (S343) ◽  
pp. 531-532
Author(s):  
M. Van de Sande ◽  
J. O. Sundqvist ◽  
T. J. Millar ◽  
D. Keller ◽  
L. Decin
Keyword(s):  
Density Distribution ◽  
Gas Phase ◽  
Thermodynamic Equilibrium ◽  
Angular Resolution ◽  
Abundance Ratio ◽  
Chemical Model ◽  
High Angular Resolution ◽  
Gas Phase Chemistry ◽  
Phase Chemistry ◽  
Smooth Density

AbstractThe chemistry within the outflow of an AGB star is determined by its elemental C/O abundance ratio. Thanks to the advent of high angular resolution observations, it is clear that most outflows do not have a smooth density distribution, but are inhomogeneous or “clumpy”. We have developed a chemical model that takes into account the effect of a clumpy outflow on its gas-phase chemistry by using a theoretical porosity formalism. The clumpiness of the model increases the inner wind abundances of all so-called unexpected species, i.e. species that are not predicted to be present assuming an initial thermodynamic equilibrium chemistry. By applying the model to the distribution of cyanopolyynes and hydrocarbon radicals within the outflow of IRC+10216, we find that the chemistry traces the underlying density distribution.

SINGULARITY FEATURES OF PORE-SIZE SOIL DISTRIBUTION: SINGULARITY STRENGTH ANALYSIS AND ENTROPY SPECTRUM

Fractals ◽  
2001 ◽  
Vol 09 (03) ◽  
pp. 305-316 ◽  
Author(s):  
F. J. CANIEGO ◽  
M. A. MARTÍN ◽  
F. SAN JOSÉ
Keyword(s):  
Image Analysis ◽  
Pore Size ◽  
Soil Samples ◽  
Second Step ◽  
Strength Analysis ◽  
Entropy Spectrum ◽  
Singular Measures ◽  
Experimental Measure ◽  
Soil Distribution ◽  
Smooth Density

In this paper, several features of pore-size soil distribution are first analyzed, suggesting that they are closer to those of singular measures than to those of distributions with smooth density. In a second step, the weighted singularity strength of an experimental measure obtained by image analysis of soil samples is evaluated. The results of this analysis show the singular nature of pore-size distribution. Finally, the distribution is characterized by means of a spectrum of entropies computed on distorted measures associated with the original experimental measure.

Soft edge magnetoplasmons in 2D systems with smooth density profile

1996 ◽  
Vol 46 (S1) ◽  
pp. 307-307 ◽  
Author(s):  
S. S. Nazin ◽  
V. B. Shikin
Keyword(s):  
Density Profile ◽  
2D Systems ◽  
Soft Edge ◽  
Smooth Density

scholarly journals A numerical study of entropy and residual entropy estimators based on smooth density estimators for non-negative random variables

2021 ◽  
Vol 54 (2) ◽  
pp. 99-121
Author(s):  
Yogendra P. Chaubey ◽  
Nhat Linh Vu
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Numerical Study ◽  
Random Variables ◽  
Random Variable ◽  
Residual Entropy ◽  
Density Estimator ◽  
Wide Range ◽  
Smooth Density

In this paper, we are interested in estimating the entropy of a non-negative random variable. Since the underlying probability density function is unknown, we propose the use of the Poisson smoothed histogram density estimator to estimate the entropy. To study the per- formance of our estimator, we run simulations on a wide range of densities and compare our entropy estimators with the existing estimators based on different approaches such as spacing estimators. Furthermore, we extend our study to residual entropy estimators which is the entropy of a random variable given that it has been survived up to time $t$.

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