Using neural networks to estimate the probability density function of transmission loss in ocean environments with a database-driven model of uncertainty

2021 ◽  
Vol 150 (4) ◽  
pp. A198-A198
Author(s):  
Brandon M. Lee ◽  
Jay R. Johnson ◽  
David R. Dowling
Keyword(s):  
Neural Networks ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Transmission Loss

Rate of Convergence in Density Estimation Using Neural Networks

1996 ◽  
Vol 8 (5) ◽  
pp. 1107-1122 ◽  
Author(s):  
Dharmendra S. Modha ◽  
Elias Masry
Keyword(s):  
Neural Networks ◽  
Probability Density Function ◽  
Probability Density ◽  
Rate Of Convergence ◽  
Density Function ◽  
Density Estimation ◽  
Exponential Family ◽  
Density Estimator ◽  
Estimation Scheme ◽  
Hidden Layer

Given N i.i.d. observations {Xi}Ni=1 taking values in a compact subset of Rd, such that p* denotes their common probability density function, we estimate p* from an exponential family of densities based on single hidden layer sigmoidal networks using a certain minimum complexity density estimation scheme. Assuming that p* possesses a certain exponential representation, we establish a rate of convergence, independent of the dimension d, for the expected Hellinger distance between the proposed minimum complexity density estimator and the true underlying density p*.

Sign in / Sign up

Export Citation Format

Share Document