A vectorized distributed parallel TMCMC random sampling algorithm

A random sampling and distributed computing technology, applied in the direction of complex mathematical operations, can solve the problems of astonishing time-consuming calculation, slow calculation speed, and limited application of Bayesian sampling correction method

Inactive Publication Date: 2019-06-25
HEFEI UNIV OF TECH
View PDF2 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] However, the existing TMCMC random sampling algorithm mainly has the following problems: First, because the posterior probability density function often has high dimensions and complex forms, especially the calculation cost of the likelihood function increases significantly with the increase of the number of sampling points and the sampling time, so the calculation The speed is slow; secondly, because TMCMC itself is based on the cha...

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A vectorized distributed parallel TMCMC random sampling algorithm
  • A vectorized distributed parallel TMCMC random sampling algorithm
  • A vectorized distributed parallel TMCMC random sampling algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0090] This embodiment is a correction of the numerical simulation of a four-layer shear-type frame model, the four-layer shear-type frame model is as follows figure 2 shown. Therefore, the system model in this case is a four-layer shear frame model, and the quality of each layer of the frame is assumed to be the same, that is, Z 1 = Z 2 = Z 3 = Z 4 =200kg, the interlayer stiffness of each layer is also the same, that is, K 1 = K 2 = K 3 = K 4 =4×10 3 N / m. The damping of the model adopts the form of Rayleigh damping, assuming that the damping ratios of the first two orders are both 0.02, that is, ξ 1 = ξ 2 = 0.02. input using the K 2 point on the form of a single point excitation. The excitation form is Gaussian white noise excitation, and then the input white noise signal and the corresponding linear time domain response output signal of each layer of the structure are used as the original monitoring data. Perform FFT transformation on the input and output sign...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to a vectorized distributed parallel TMCMC random sampling algorithm, which comprises the following steps of: vectorizing an objective function through an element operation modeand a matrix operation mode to obtain a vectorized objective function, namely a vectorized posterior probability density function; building a distributed computing platform based on an MATLAB (MatrixLaboratory) Distributive ComputingServer tool box, and building a distributed computing platform based on the MATLAB Distributive ComputingServer tool box; And running a vectorized distributed parallel TMCMC algorithm on the distributed computing platform so as to solve a target function. According to the method, on the premise that the calculation precision is guaranteed, the calculation efficiency is greatly improved, and the calculation time is shortened.

Description

technical field [0001] The invention belongs to the random sampling algorithm technology in the Bayesian method, in particular to a vectorized distributed parallel TMCMC random sampling algorithm. Background technique [0002] When using Bayesian theory for statistical inference, theoretically speaking, for any prior distribution, it is only necessary to calculate the characteristics of the required posterior distribution according to Bayesian theory, such as the moment of the posterior distribution (the posterior mean , posterior variance), posterior probability density function, etc.; its essence is to calculate the high-dimensional integral of the function involved in the posterior distribution. However, in practical applications, the monitoring data is often scarce, and the posterior distribution of unknown parameters is often a high-dimensional, complex and uncommon distribution, which is very difficult to calculate. The random sampling method can break through this ex...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F17/18
Inventor 曹诗泽颜王吉任伟新
Owner HEFEI UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap