This function computes the forward log-likelihood of data given a model.
See also:
Up: IndexOur Markov model is a graph with colored vertices. A vertex is called a "state," and its color is a nonnegative integer called its "class." States in the same class are indistinguishable (same amp). To describe the vertices, provide the array
int clazz[Ns] = [class of each state] int Ns = number of states
Each edge is labeled with its transition rate (probability per second). These form the matrix
Q, a Ns x Ns matrix with
\(Q_{a,b}\) = rate from state a to state b
\(Q_{a,a} = - \sum_i Q_{a,i}\) where \(i \neq a\)
Each \(Q_{a,b} = K0_{a,b} * Ligand_{a,b} * e^{K1_{a,b} * Voltage_{a,b}}\). You provide the Ns x Ns matrices
double **K0, **K1 of kinetic parameters int **Ligand, **Voltage index of the ligand or voltage signal influencing each rate, or 0
with the diagonals undefined.
A pair of states is either connected in both directions or neither. To indicate un-connectedness, set
\(K0_{a,b} = K0_{b,a} = 0\)
For constant state entry probabilities, as described in (Qin...1996), provide the array
int P0[Ns]
For equilibrium entry probabilities, as described in (Milescu...2005), provide
P0 = NULL
For data, you provide one or more idealized segments. A segment is described by
int DwellCount number of events int Classes[DwellCount] class index of each event float Durations[DwellCount] length of each dwell in seconds, less tdead
and you give the segments together as
int Nseg number of segments int **dwellCounts int **classeses float **durationses
tdead is the longest duration of events that can't be reliably detected. MIL assumes you have deleted any such events, by merging them with their prior. Also, for computational reasons, tdead should be subtracted from each event. We provide a utility which merges and shortens events, in-place:
end_html */ extern "C" MAXILL_API void __stdcall mil_prep_events( int *dwellCount, int *classes, float *durations, double tdead_ms ); extern "C" MAXILL_API void __stdcall mil_prep_segments( int Nseg, int *dwellCounts, int **classeses, float **durationses, double tdead_ms ); /* begin_htmlIf any \(Ligand_{a,b}\ \neq 0\) or \(Voltage_{a,b}\ \neq 0\), you must provide an array
double *Constants
where e.g. if \(Ligand_{a,b} = 2\) then Constants[2] holds the ligand concentration. We assume all segments were recorded at the same constant conditions. For global fitting, call max_inter_ll separately for each dataset, with different constants, and sum the LL.
double *LL: upon return, contains log(prob. that model generated this data)
Returns: 0 on success, error codes to be defined.