timproved wording - cosmo - front and backend for Markov-Chain Monte Carlo inversion of cosmogenic nuclide concentrations
(HTM) git clone git://src.adamsgaard.dk/cosmo
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(DIR) commit a53b33ee8911d15bcfd27b48dad8944d0fd86baa
(DIR) parent 1d8bf4c603bb8c5c9084c8c7e3c82d0f6fe5a87d
(HTM) Author: Anders Damsgaard <anders.damsgaard@geo.au.dk>
Date: Fri, 27 Nov 2015 16:46:59 +0100
improved wording
Diffstat:
M pages/methods.html | 51 ++++++++++++++++---------------
1 file changed, 26 insertions(+), 25 deletions(-)
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(DIR) diff --git a/pages/methods.html b/pages/methods.html
t@@ -62,19 +62,18 @@
with uniform probability across the logarithmic parameter
interval. The temporal parameter (<i>t</i><sub>degla</sub>)
and climate record threshold value
- (δ<sup>18</sup>O<sub>threshold</sub>) is tested with
+ (δ<sup>18</sup>O<sub>threshold</sub>) are tested with
uniform probability across the linear parameter interval.
- The user specifies the bounds of the model parameters.
</p>
<p>When model parameters
(ε<sub>int</sub>, ε<sub>gla</sub>,
<i>t</i><sub>degla</sub>,
δ<sup>18</sup>O<sub>threshold</sub>) are varied within
- specified limits, they can be thought of as orthogonal axes
- creating a coordinate system in higher-order space. Every
- position in this model space is associated with a certain
- set of model parameter values.
+ specified limits, they can be thought of as being orthogonal
+ axes spanning a coordinate system in four-dimensional space.
+ Each position in this model space is associated with a
+ unique set of model parameter values.
</p>
<p>Given a single value of model parameters
t@@ -86,7 +85,7 @@
computed. This <i>forward model</i> describes a possible
history of exhumation and TCN production in a sample volume
as it experiences the variable physical environment of the
- Pleistocene.</p>
+ Quaternary.</p>
</div>
t@@ -105,7 +104,9 @@
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.</p>
+ 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">
t@@ -121,8 +122,8 @@
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 along
- approximately in the same direction in model space.
+ the observed dataset, it continues walking in the same
+ direction in model space.
</p>
<p>
t@@ -142,19 +143,19 @@
<p>
For a given observational data set more than one set of
model parameters may produce forward models which
- sufficiently satisfy the MCMC walker as solution
- approximations. 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 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 will find the area of better model parameters.
+ 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 will
+ find the area of better model parameters.
</p>
<p>
t@@ -162,8 +163,8 @@
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 reliable results,
- the number of walkers should be increased (3 to 4).
+ When attempting to produce high-quality and reliable
+ results, the number of walkers should be increased (3 to 4).
</p>
</div>