Computing the survival probability density function in jump-diffusion models: A new approach based on radial basis functions

2011 ◽  
Vol 35 (9) ◽  
pp. 1075-1084 ◽  
Author(s):  
Luca Vincenzo Ballestra ◽  
Graziella Pacelli
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Radial Basis Functions ◽  
Survival Probability ◽  
Jump Diffusion ◽  
Diffusion Models ◽  
Basis Functions ◽  
New Approach ◽  
Radial Basis

scholarly journals Estimating survival probability using the terrestrial extinction history for the search for extraterrestrial life

2020 ◽  
Author(s):  
Kohji Tsumura
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Survival Probability ◽  
Normal Function ◽  
Extraterrestrial Life ◽  
New Approach ◽  
Random Multiplicative Process ◽  
Log Normal ◽  
Life On Earth ◽  
Fitted Function

Several exoplanets have been discovered to date, and the next step is the search for extraterrestrial life. However, it is difficult to estimate the number of life-bearing exoplanets because our only template is based on life on Earth. In this paper, a new approach is introduced to estimate the probability that life on Earth has survived from birth to the present based on its terrestrial extinction history. A histogram of the extinction intensity during the Phanerozoic Eon is modeled effectively with a log-normal function, supporting the idea that terrestrial extinction is a random multiplicative process. Assuming that the fitted function is a probability density function of extinction intensity per unit time, the estimated survival probability of life on Earth is ~0.15 from the beginning of life to the present. This value can be a constraint on fi in the Drake equation, which contributes to estimating the number of life-bearing exoplanets.

A new approach to analysis of residue probability density function in pipelined ADCs

Integration ◽  
2016 ◽  
Vol 52 ◽  
pp. 51-61 ◽  
Author(s):  
Esmaeil Fatemi-Behbahani ◽  
Ebrahim Farshidi ◽  
Karim Ansari-Asl
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
New Approach ◽  
Pipelined Adcs

CRITICAL SWITCHING BEHAVIOUR IN SPARSELY POPULATED SYSTEMS

2003 ◽  
Vol 17 (31n32) ◽  
pp. 5893-5904
Author(s):  
RALF METZLER
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Survival Probability ◽  
Switching Time ◽  
Kinematic Behaviour ◽  
Threshold Switch ◽  
Sparse Population ◽  
The Stability ◽  
Modelling Approach

The basic kinematic behaviour of a threshold switch in a system with a sparse population is investigated. We determine the basic quantities such as the number probability density function, the survival probability, the characteristic switching time, and the response to external triggering of the switch. The modelling approach is then extended to systems with response retardation, which, it is argued, may improve the stability of the switch.

Reserch on a NOx Emission Forecasting Method Based on Naïve Bayesian

2014 ◽  
Vol 936 ◽  
pp. 1857-1861
Author(s):  
Zhang Lin ◽  
Wang Xin ◽  
Zhang Qi
Keyword(s):  
Diesel Engine ◽  
Probability Density Function ◽  
Environmental Pollution ◽  
Probability Density ◽  
Density Function ◽  
Nox Emission ◽  
New Approach ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Forecasting Method

Increasingly serious environmental pollution,trying to find a effective method to control NOx emission become more importance. Under this background, this paper adopts the naïve Bayesian classifier method which built on the basis of the probability density function to forecasting the NOx emission of diesel engine. This paper proposes a new approach to weight the super-parent one dependence estimators, and uses the UCI datasets to verify the validity of the proposed method. Finally, apply this diagnosis technology to the collected WD615 diesel engine data. The comparison experiments with other algorithms demonstrate the effectiveness of the proposed method.

scholarly journals Numerical Algorithms for Estimating Probability Density Function Based on the Maximum Entropy Principle and Fup Basis Functions

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1559
Author(s):  
Nives Brajčić Kurbaša ◽  
Blaž Gotovac ◽  
Vedrana Kozulić ◽  
Hrvoje Gotovac
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Maximum Entropy ◽  
Density Function ◽  
Moment Problem ◽  
Statistical Power ◽  
Maximum Entropy Principle ◽  
Basis Functions ◽  
Approximation Properties ◽  
Hybrid Strategy

Estimation of the probability density function from the statistical power moments presents a challenging nonlinear numerical problem posed by unbalanced nonlinearities, numerical instability and a lack of convergence, especially for larger numbers of moments. Despite many numerical improvements over the past two decades, the classical moment problem of maximum entropy (MaxEnt) is still a very demanding numerical and statistical task. Among others, it was presented how Fup basis functions with compact support can significantly improve the convergence properties of the mentioned nonlinear algorithm, but still, there is a lot of obstacles to an efficient pdf solution in different applied examples. Therefore, besides the mentioned classical nonlinear Algorithm 1, in this paper, we present a linear approximation of the MaxEnt moment problem as Algorithm 2 using exponential Fup basis functions. Algorithm 2 solves the linear problem, satisfying only the proposed moments, using an optimal exponential tension parameter that maximizes Shannon entropy. Algorithm 2 is very efficient for larger numbers of moments and especially for skewed pdfs. Since both Algorithms have pros and cons, a hybrid strategy is proposed to combine their best approximation properties.

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