A new probability density function in earthquake occurrences
Authors
Abstract:
Although knowing the time of the occurrence of the earthquakes is vital and helpful, unfortunately it is still unpredictable. By the way there is an urgent need to find a method to foresee this catastrophic event. There are a lot of methods for forecasting the time of earthquake occurrence. Another method for predicting that is to know probability density function of time interval between earthquakes. In this paper a new probability density function (PDF) for the time interval between earthquakes is found out. The parameters of the PDF will be estimated, and ultimately, the PDF will be tested by the earthquakes data about Iran.
similar resources
a new probability density function in earthquake occurrences
although knowing the time of the occurrence of the earthquakes is vital and helpful, unfortunately it is still unpredictable. by the way there is an urgent need to find a method to foresee this catastrophic event. there are a lot of methods for forecasting the time of earthquake occurrence. another method for predicting that is to know probability density function of time interval between earth...
full textProbability Density Function Measurement
We report experimental results on the acceleration component probability distribution function at R λ = 690 to probabilities of less than 10 −7. This is an improvement of more than an order of magnitude over past measurements and allows us to conclude that the fourth moment converges and the flatness is approximately 55. We compare our probability distribution to those predicted by several mode...
full textON THE STATIONARY PROBABILITY DENSITY FUNCTION OF BILINEAR TIME SERIES MODELS: A NUMERICAL APPROACH
In this paper, we show that the Chapman-Kolmogorov formula could be used as a recursive formula for computing the m-step-ahead conditional density of a Markov bilinear model. The stationary marginal probability density function of the model may be approximated by the m-step-ahead conditional density for sufficiently large m.
full textProbability Density Function Estimation using theMinMax
| The problem of initial probability assignment consistent with the available information about a probabilis-tic system is called a direct problem. Jaynes' maximum en-tropy principle (MaxEnt) provides a method for solving direct problems when the available information is in the form of moment constraints. On the other hand, given a probability distribution, the problem of nding a set of constra...
full textExperimental Lagrangian Acceleration Probability Density Function Measurement
We report experimental results on the acceleration component probability distribution function at R λ = 690 to probabilities of less than 10 −7. This is an improvement of more than an order of magnitude over past measurements and allows us to conclude that the fourth moment converges and the flatness is approximately 55. We compare our probability distribution to those predicted by several mode...
full textQuantum Computation Based Probability Density Function Estimation
Signal processing techniques will lean on blind methods in the near future, where no redundant, resource allocating information will be transmitted through the channel. To achieve a proper decision, however, it is essential to know at least the probability density function (pdf), which to estimate is classically a time consumpting and/or less accurate hard task, that may make decisions to fail....
full textMy Resources
Journal title
volume 4 issue 6
pages 1- 6
publication date 2008-06-01
By following a journal you will be notified via email when a new issue of this journal is published.
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023