Modeling:Staircase

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Staircase estimates rate constants for staircase processes, where the observed variable moves in one direction, with each step governed by the same Markov process.

The details are explained in Dr. Lorin Milescu's thesis.

Properties

Data channel index which A/D channel contains the idealized data, typically 0
Quiet Output print dramatically less info to the Report window
Data source
Ligand, Voltage, ... experimental conditions as needed by the model

A variable can have a "Value" or take its changing value from an A/D channel. The "Conditioning" value is used to calculate conditioning equilibrium probability.

Add/Delete/Presets (ignore these) add/delete experimental variables, and load/save all variables
Use (column when Data source is File list)

whether each file will be part of the file list

LL conv stop if LL increases by less than this much
Grad conv stop if all gradients are less than this much
Max iter at most how many times to repeat (calculate LL and gradient, modify parameters)
Max step how much to change parameters each iteration. 1.0 is the natural step size; smaller numbers may be more reliable for sensitive models, but will converge more slowly.
Max power Staircase runs faster by combining 2n adjacent identical data points, for the largest possible n < = Maxpower. In the algorithm, A = eQΔt, B is the diagonal observation-probability matrix of 1s and 0s, and St + 1 = St * A * B is how the forward probability advances. The max power optimization advances 2n points at once by S_{t+2^n} = S_t * (A*B)^n. If you are experiencing numerical instabilities, or wish to rule them out, set Max power to 0
Min rate
Run mode optimize (maximize LL) or check (compute LL with current parameters)
Max jump
Right only
Batch (segments) Together: Runs MIP with all the data at once, summing LL and generating one final model
In groups of:
In groups of:
Max batch
Identical segs
MUX files
Presets


Results

A ton of relevant info is shown in the Report window. (explain please)


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