Probability Model of Beijing Residentials’ Fire Load

2011 ◽  
Vol 368-373 ◽  
pp. 993-1002 ◽  
Author(s):  
Yan Chao Liu ◽  
Dong Dong Liu ◽  
Jin Ping Wang ◽  
Wei Hong Chen ◽  
Bin Zhao
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Probability Model ◽  
Estimation Method ◽  
Extreme Value Distribution ◽  
Likelihood Estimation ◽  
Generalized Extreme Value Distribution ◽  
Fire Load ◽  
Distribution Models

We analyses 418 of fire load data of Beijing residential in city subdivision and suburban district which collected by Beijing University of Civil Engineering and Architecture and Institute of Building Fire Research. Suppose the probability density function of several fire load, using maximum likelihood estimation method to obtain the parameters, and use the K-S test examine the probability density function model, the final selections of Generalized extreme value distribution and Log logistic fit better as a Beijing residential fire load distribution probability distribution models. Finally using these models, according to the JCSS rules, the fire load standard value of Beijing residential is put forward.

scholarly journals Maximum-likelihood estimation of the parameters of a four-parameter class of probability distributions

1988 ◽  
Vol 31 (2) ◽  
pp. 271-283 ◽  
Author(s):  
Siegfried H. Lehnigk
Keyword(s):  
Maximum Likelihood ◽  
Probability Density Function ◽  
Maximum Likelihood Estimation ◽  
Probability Density ◽  
Density Function ◽  
Probability Distributions ◽  
Likelihood Estimation ◽  
Initial Shape ◽  
Definition Of ◽  
Image Position

We shall concern ourselves with the class of continuous, four-parameter, one-sided probability distributions which can be characterized by the probability density function (pdf) classIt depends on the four parameters: shift c ∈ R, scale b > 0, initial shape p < 1, and terminal shape β > 0. For p ≦ 0, the definition of f(x) can be completed by setting f(c) = β/bΓ(β−1)>0 if p = 0, and f(c) = 0 if p < 0. For 0 < p < 1, f(x) remains undefined at x = c; f(x)↑ + ∞ as x↓c.

Fully Nonparametric Probability Density Function Estimation Based on Empirical Mode Decomposition

2012 ◽  
Vol 460 ◽  
pp. 189-192
Author(s):  
Hong Ying Hu ◽  
Chun Ming Kan
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Density Estimation ◽  
Empirical Mode Decomposition ◽  
Estimation Method ◽  
Function Estimation ◽  
Mode Decomposition ◽  
Non Stationary Signal ◽  
Density Function Estimation

Empirical Mode Decomposition (EMD) is a non-stationary signal processing method developed recently. It has been applied in many engineering fields. EMD has many similarities with wavelet decomposition. But EMD Decomposition has its own characteristics, especially in accurate rend extracting. Therefore the paper firstly proposes an algorithm of extracting slow-varying trend based on EMD. Then, according to wavelet probability density function estimation method, a new density estimation method based on EMD is presented. The simulations of Gaussian single and mixture model density estimation prove the advantages of the approach with easy computation and more accurate result

A points estimation and series approximation method for uncertainty analysis

2009 ◽  
Vol 223 (9) ◽  
pp. 1997-2007 ◽  
Author(s):  
X F Zhang ◽  
Y E Zhao ◽  
Y M Zhang ◽  
X Z Huang ◽  
H Li
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Function Approximation ◽  
Structural Reliability ◽  
Estimation Method ◽  
Point Estimation ◽  
Performance Function ◽  
Density Function Approximation ◽  
Point Estimation Method

The objective of this article is to present an algorithm for moment evaluation and probability density function approximation of performance function for structural reliability analysis. In doing so, a point estimation method for probability moment of performance function is discussed at first. Based on the coherent relationship between the orthogonal polynomial and probability density function, formulas for point estimation are derived. Vector operators are defined to alleviate computational burden for computer programming. Then, by utilizing C-type Gram—Charlier series expansion method, a procedure for probability density function approximation of the performance function is studied. At last, the accuracy of the proposed method is demonstrated using three numerical examples.

scholarly journals QUANTUM COMPUTATION BASED PROBABILITY DENSITY FUNCTION ESTIMATION

2005 ◽  
Vol 03 (01) ◽  
pp. 93-98
Author(s):  
FERENC BALÁZS ◽  
SÁNDOR IMRE
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Estimation Method ◽  
Function Estimation ◽  
Pdf Estimation ◽  
Density Function Estimation ◽  
Processing Techniques ◽  
Near Future ◽  
Proper Setting

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. This paper describes the design of a quantum assisted PDF estimation method also by way of an example, which promises to achieve the exact PDF by proper setting of parameters in a very rapid way.

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