scholarly journals Weak convergence of marked point processes generated by crossings of multivariate jump processes. Applications to neural network modeling

2014 ◽  
Vol 288 ◽  
pp. 45-52 ◽  
Author(s):  
Massimiliano Tamborrino ◽  
Laura Sacerdote ◽  
Martin Jacobsen
Keyword(s):  
Neural Network ◽  
Weak Convergence ◽  
Point Processes ◽  
Marked Point ◽  
Network Modeling ◽  
Jump Processes ◽  
Marked Point Processes ◽  
Neural Network Modeling

Integral equations for compound distribution functions

1996 ◽  
Vol 33 (2) ◽  
pp. 388-399 ◽  
Author(s):  
Christian Max Møller
Keyword(s):  
Point Processes ◽  
Marked Point ◽  
Building Blocks ◽  
Distribution Functions ◽  
Jump Processes ◽  
Marked Point Processes ◽  
Martingale Theory ◽  
Compound Distribution ◽  
Change Of Variable ◽  
Fixed Period

The aim of the present paper is to introduce some techniques, based on the change of variable formula for processes of finite variation, for establishing (integro) differential equations for evaluating the distribution of jump processes for a fixed period of time. This is of interest in insurance mathematics for evaluating the distribution of the total amount of claims occurred over some period of time, and attention will be given to such issues. Firstly we will study some techniques when the process has independent increments, and then a more refined martingale technique is discussed. The building blocks are delivered by the theory of marked point processes and associated martingale theory. A simple numerical example is given.

Integral equations for compound distribution functions

1996 ◽  
Vol 33 (02) ◽  
pp. 388-399
Author(s):  
Christian Max Møller
Keyword(s):  
Point Processes ◽  
Marked Point ◽  
Building Blocks ◽  
Distribution Functions ◽  
Jump Processes ◽  
Marked Point Processes ◽  
Martingale Theory ◽  
Compound Distribution ◽  
Change Of Variable ◽  
Fixed Period

The aim of the present paper is to introduce some techniques, based on the change of variable formula for processes of finite variation, for establishing (integro) differential equations for evaluating the distribution of jump processes for a fixed period of time. This is of interest in insurance mathematics for evaluating the distribution of the total amount of claims occurred over some period of time, and attention will be given to such issues. Firstly we will study some techniques when the process has independent increments, and then a more refined martingale technique is discussed. The building blocks are delivered by the theory of marked point processes and associated martingale theory. A simple numerical example is given.

Water quality parameter forecast based on grey neural network modeling

2009 ◽  
Vol 29 (6) ◽  
pp. 1529-1531 ◽  
Author(s):  
Wei-ren SHI ◽  
Yan-xia WANG ◽  
Yun-jian TANG ◽  
Min FAN
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Network Modeling ◽  
Quality Parameter ◽  
Water Quality Parameter ◽  
Neural Network Modeling

ε-Insensitive Criterion and Structure Risk Based Radius-basis-function Neural-network Modeling

2012 ◽  
Vol 34 (6) ◽  
pp. 1414-1419
Author(s):  
Qing-bing Sang ◽  
Zhao-hong Deng ◽  
Shi-tong Wang ◽  
Xiao-jun Wu
Keyword(s):  
Neural Network ◽  
Basis Function ◽  
Network Modeling ◽  
Neural Network Modeling
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