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- Title
Bayesian compendium.
- Abstract
It is given as HT <math display="inline" altimg="urn:x-wiley:0006341X:media:biom13676:biom13676-math-0001" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>z</mi><mspace /><mo>=</mo><mspace /><mi>y</mi><mo>+</mo><mspace /><msub><mo> </mo><mi>y</mi></msub></mrow><annotation encoding="WINDOWS-1252">$z\; = \;y + \;{ \in y}$</annotation></semantics></math> ht , where I z i is the true value, I y i is the measured value, and HT <math display="inline" altimg="urn:x-wiley:0006341X:media:biom13676:biom13676-math-0002" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msub><mo> </mo><mi>y</mi></msub><annotation encoding="application/x-tex">${ \in y}$</annotation></semantics></math> ht is the measurement error. It should really be written as HT <math display="inline" altimg="urn:x-wiley:0006341X:media:biom13676:biom13676-math-0003" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mspace /><mo>=</mo><mspace /><mi>z</mi><mo>+</mo><msub><mo> </mo><mi>y</mi></msub></mrow><annotation encoding="application/x-tex">$y\; = \;z + { \in y}$</annotation></semantics></math> ht , as "measured value = truth + error.".
- Subjects
KALMAN filtering; MARKOV chain Monte Carlo; GAUSSIAN Markov random fields; STATISTICAL models; BAYES' theorem
- Publication
Biometrics, 2022, Vol 78, Issue 2, p813
- ISSN
0006-341X
- Publication type
Article
- DOI
10.1111/biom.13676