tchange nesting and indentation - cosmo - front and backend for Markov-Chain Mo… | |
git clone git://src.adamsgaard.dk/cosmo | |
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--- | |
commit 9abb8d8302f7752b7f803bacc0a0a68e3992ee00 | |
parent a53b33ee8911d15bcfd27b48dad8944d0fd86baa | |
Author: Anders Damsgaard <[email protected]> | |
Date: Fri, 27 Nov 2015 16:48:47 +0100 | |
change nesting and indentation | |
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M pages/methods.html | 158 ++++++++++++++++-------------… | |
1 file changed, 81 insertions(+), 77 deletions(-) | |
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diff --git a/pages/methods.html b/pages/methods.html | |
t@@ -87,85 +87,89 @@ | |
as it experiences the variable physical environment of the | |
Quaternary.</p> | |
- </div> | |
- | |
- <div id="twostage" class="subsection scrollspy"> | |
- <h4 class="header blue-text light"> | |
- Two-stage glacial-interglacial forward model</h4> | |
- <p>The forward model builds on the assumption of a | |
- "two-stage uniformitarianism", meaning that the processes | |
- that operated during the Holocene also operated during | |
- earlier interglacials with comparable intensity. Likewise, | |
- the erosion rate during the past glacial periods is assumed | |
- to be comparable.</p> | |
- | |
- <p>The model approach assumes that glacial periods were | |
- characterized by 100% shielding and no exposure, which wou… | |
- require more than 10 m of ice thickness for production due | |
- to spallation (>50 m for muons). Interglacial periods a… | |
- assumed to have been characterized by 100% exposure and ze… | |
- shielding. The production of TCNs takes place during the | |
- interglacials, while erosion removes the land surface at | |
- different rates during the glacials and interglacials.</p> | |
- </div> | |
- | |
- <div id="mcmcwalker" class="subsection scrollspy"> | |
- <h4 class="header blue-text light"> | |
- What is a MCMC walker?</h4> | |
- <p> | |
- A MCMC walker is in this context a numerical entity which | |
- sequentially explores the model parameter space in order to | |
- obtain the closest match between the forward model and the | |
- observational dataset of TCNs. During each iteration | |
- the walker takes its current position in model space, plugs | |
- the parameter value into the forward model, and | |
- evaluates if the output result matches the observational | |
- record better or worse than the output at its previous | |
- position in model space. If the new results better matches | |
- the observed dataset, it continues walking in the same | |
- direction in model space. | |
- </p> | |
- <p> | |
- Starting at a random place inside the model space, a burn-… | |
- phase of 1000 iterations is first used to make a crude | |
- search of the entire model space. The burn-in phase is | |
- followed by a similar but more detailed and local search of | |
- the model space, based on the best-fit model parameters fr… | |
- the burn-in phase. The weighted least-squared misfit to | |
- observed TCN concentrations is used to evaluate the | |
- likelyhood for the combinations of model parameter values. | |
- The MCMC walker continues exploring the model space until … | |
- is sufficiently satisfied with the best model parameter | |
- estimate it has found. | |
- </p> | |
- | |
- <p> | |
- For a given observational data set more than one set of | |
- model parameters may produce forward models which | |
- sufficiently satisfy the MCMC walker. | |
- In this case the solution is <i>non-unique</i>. Even worse, | |
- a single MCMC walker may find an area in model space which | |
- seemingly is in good correspondence with the observational | |
- data set, but the walker is missing a much better set of | |
- model parameters since they are located somewhere entirely | |
- different in the model space. In order to mitigate these | |
- issues, MCMC inversions are often performed using several | |
- MCMC walkers. The starting point of each MCMC walker is | |
- chosen at random, resulting in unique walks through the | |
- model space. If a single walker is caught in an area of | |
- non-ideal solutions, chances are that the other walkers wi… | |
- find the area of better model parameters. | |
- </p> | |
+ <div id="twostage" class="subsection scrollspy"> | |
+ <h4 class="header blue-text light"> | |
+ Two-stage glacial-interglacial forward model</h4> | |
+ <p>The forward model builds on the assumption of a | |
+ "two-stage uniformitarianism", meaning that the | |
+ processes that operated during the Holocene also | |
+ operated during earlier interglacials with comparable | |
+ intensity. Likewise, the erosion rate during the past | |
+ glacial periods is assumed to be comparable.</p> | |
+ | |
+ <p>The model approach assumes that glacial periods were | |
+ characterized by 100% shielding and no exposure, which | |
+ would require more than 10 m of ice thickness for | |
+ production due to spallation (>50 m for muons). | |
+ Interglacial periods are assumed to have been | |
+ characterized by 100% exposure and zero shielding. The | |
+ production of TCNs takes place during the interglacial… | |
+ while erosion removes the land surface at different | |
+ rates during the glacials and interglacials.</p> | |
+ </div> | |
- <p> | |
- The computational time depends on the number of MCMC | |
- walkers. When casually trying out the calculator we | |
- recommend using low numbers of MCMC walkers (1 to 2) in | |
- order to obtain fast results and reduce load on the server. | |
- When attempting to produce high-quality and reliable | |
- results, the number of walkers should be increased (3 to 4… | |
- </p> | |
+ <div id="mcmcwalker" class="subsection scrollspy"> | |
+ <h4 class="header blue-text light"> | |
+ What is a MCMC walker?</h4> | |
+ <p> | |
+ A MCMC walker is in this context a numerical entity | |
+ which sequentially explores the model parameter space … | |
+ order to obtain the closest match between the forward | |
+ model and the observational dataset of TCNs. During ea… | |
+ iteration the walker takes its current position in mod… | |
+ space, plugs the parameter value into the forward mode… | |
+ and evaluates if the output result matches the | |
+ observational record better or worse than the output at | |
+ its previous position in model space. If the new resul… | |
+ better matches the observed dataset, it continues | |
+ walking in the same direction in model space. | |
+ </p> | |
+ | |
+ <p> | |
+ Starting at a random place inside the model space, a | |
+ burn-in phase of 1000 iterations is first used to make… | |
+ crude search of the entire model space. The burn-in | |
+ phase is followed by a similar but more detailed and | |
+ local search of the model space, based on the best-fit | |
+ model parameters from the burn-in phase. The weighted | |
+ least-squared misfit to observed TCN concentrations is | |
+ used to evaluate the likelyhood for the combinations of | |
+ model parameter values. The MCMC walker continues | |
+ exploring the model space until it is sufficiently | |
+ satisfied with the best model parameter estimate it has | |
+ found. | |
+ </p> | |
+ | |
+ <p> | |
+ For a given observational data set more than one set of | |
+ model parameters may produce forward models which | |
+ sufficiently satisfy the MCMC walker. In this case the | |
+ solution is <i>non-unique</i>. Even worse, a single MC… | |
+ walker may find an area in model space which seemingly | |
+ is in good correspondence with the observational data | |
+ set, but the walker is missing a much better set of | |
+ model parameters since they are located somewhere | |
+ entirely different in the model space. In order to | |
+ mitigate these issues, MCMC inversions are often | |
+ performed using several MCMC walkers. The starting | |
+ point of each MCMC walker is chosen at random, resulti… | |
+ in unique walks through the model space. If a single | |
+ walker is caught in an area of non-ideal solutions, | |
+ chances are that the other walkers will find the area … | |
+ better model parameters. | |
+ </p> | |
+ | |
+ <p> | |
+ The computational time depends on the number of MCMC | |
+ walkers. When casually trying out the calculator we | |
+ recommend using low numbers of MCMC walkers (1 to 2) in | |
+ order to obtain fast results and reduce load on the | |
+ server. When attempting to produce high-quality and | |
+ reliable results, the number of walkers should be | |
+ increased (3 to 4). | |
+ </p> | |
+ </div> | |
</div> | |