Description Usage Arguments Value See Also
Or the log likelihood of the observation, given graph with parameters, depending how things are modeled.
Basically this is just cost_function
that doesn't optimize the edge variables
but has them as an argument instead.
1 2  log_likelihood(f, concentration, matrix, graph,
parameters = extract_graph_parameters(graph))

f 
The observed f statistics (the column 
concentration 
The Cholesky decomposition of the inverted covariance matrix. So if S is the covariance matrix, this is C = chol(S^(1)) satisfying S^(1) = C^t*C. 
matrix 
A column reduced edge optimisation matrix (typically given by the function

graph 
The admixture graph. Here to give default value for: 
parameters 
Just because we need to know variable names. 
The output is a function. Given admixture proportions x
and edge lengths e
, the graph
topology T implies an estimate F for the statistics f. This output function
calculates
l = (Ff)^t*S^(1)*(Ff)
from x
and e
. Up to a constant error and multiplier that is a log likelihood function, as
det(2*π*S)^(1/2)*exp(l/2)
can be seen as a likelihood of a graph with parameters, given the observation, or the other way around (possibly up to a normalization constant).
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