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[DOC] Adds PCA documentation change that was missing #554

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merged 9 commits into from
Apr 1, 2020
Merged

[DOC] Adds PCA documentation change that was missing #554

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7288735
[DOC] Updates the TEDPCA section following changes in tedana v0.0.8
eurunuela Mar 21, 2020
66e028f
Adds missing reference
eurunuela Mar 21, 2020
da7fa34
Adds missing line break
eurunuela Mar 21, 2020
48958c9
Reduced the math-wizardry of the MA PCA description
eurunuela Mar 23, 2020
3b4c570
Gives iid its full name
eurunuela Mar 24, 2020
1b68ebd
Update docs/approach.rst
eurunuela Mar 24, 2020
510cce5
Update docs/approach.rst
eurunuela Mar 24, 2020
8ff4c58
Assess PR review comments
eurunuela Mar 24, 2020
7a68b9c
Merge branch 'master' into enh/doc_pca
eurunuela Mar 25, 2020
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2 changes: 1 addition & 1 deletion docs/approach.rst
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Expand Up @@ -210,7 +210,7 @@ Here we can see time series for some example components (we don't really care ab
These components are subjected to component selection, the specifics of which
vary according to algorithm. Specifically, ``tedana`` offers two different approaches that perform this step.

The default approach (the `mdl` option for `--tedpca`) is based on a Moving Average (stationary Gaussian) process
The simplest approach (the default `mdl`, `aic` and `kic` options for `--tedpca`) is based on a Moving Average (stationary Gaussian) process
proposed by `Li et al (2007)`_. A moving average process is the output of a linear system (which in this case is
a smoothing filter) that has an independent and identically distributed Gaussian process as the input. If we assume that the linear system is shift
invariant, the moving average process is a stationary Gaussian random process. Simply put, this process more optimally
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