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Gibbs sampling for a bayesian hierarchical general linear model
Alicia A. Johnson,
Galin L. Jones
Statistics (Twin Cities)
Research output
:
Contribution to journal
›
Article
›
peer-review
21
Scopus citations
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Dive into the research topics of 'Gibbs sampling for a bayesian hierarchical general linear model'. Together they form a unique fingerprint.
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Mathematics
Hierarchical Linear Models
100%
General Linear Model
80%
Gibbs Sampling
75%
Gibbs Sampler
75%
Health
44%
Batch Means
32%
Geometric Ergodicity
29%
Ergodic Averages
29%
Invariant Distribution
26%
Maintenance
24%
Posterior distribution
21%
Central limit theorem
19%
Estimate
19%
Asymptotic distribution
18%
Gaussian distribution
18%
Requirements
17%
Costs
15%
Observation
13%
Converge
13%
Business & Economics
Gibbs Sampler
96%
Gibbs Sampling
92%
Central Limit Theorem
36%
Inference
35%
Invariant Distribution
35%
Ergodicity
31%
Posterior Distribution
27%
Health Maintenance Organizations
27%
Health Plans
24%
General Theory
24%
Normal Distribution
23%
Batch
21%
Costs
8%