Skip to main navigation
Skip to search
Skip to main content
Arizona State University Home
Home
Profiles
Departments and Centers
Scholarly Works
Activities
Equipment
Grants
Datasets
Prizes
Search by expertise, name or affiliation
Simulation-based Bayesian optimal design for multi-factor accelerated life tests
Ehab Nasir,
Rong Pan
Computing and Augmented Intelligence, School of (IAFSE-SCAI)
Assured and Scalable Data Engineering, Center for (CASCADE)
Computer Science and Engineering
Industrial, Systems and Operations Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
4
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Simulation-based Bayesian optimal design for multi-factor accelerated life tests'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Mathematics
Bayesian Optimal Design
100%
Accelerated Life Test
85%
Simulation
38%
Latin Hypercube Sampling
23%
Nonparametric Smoothing
23%
Expected Utility
20%
Smoothing Techniques
20%
Utility Function
18%
Markov Chain Monte Carlo Methods
17%
Computational Cost
15%
Grid
12%
Optimization
11%
Costs
11%
Design
9%
Framework
8%
Engineering & Materials Science
Monte Carlo methods
66%
Cost reduction
62%
Markov chains
60%
Sampling
43%
Costs
23%