&$     NoneVA more general solver. This directly solves the lagrangian of the constraints and the D the additional constraint that the probabilities must sum to one. #Tolerance for the numerical solver the count of probabilities  constraints GEither the a discription of what wrong or the probability distribution NoneNone>A function that takes an index and value and returns a value.  See  and   for examples. 8Constraint type. A function and the constant it equals. Think of it as the pair (f, c) in the constraint     p  f(x ) = c +such that we are summing over all values . +For example, for a variance constraint the f would be (\ x -> x*x) and c would be the variance. SDiscrete maximum entropy solver where the constraints are all moment constraints.   #Tolerance for the numerical solver &values that the distributions is over The constraints HEither the a discription of what wrong or the probability distribution     None$This is for the linear case Ax = b  x2 in this situation is the vector of probablities. ?Consider the 1 dimensional circular convolution using hmatrix. import Numeric.LinearAlgebraffromLists [[0.68, 0.22, 0.1], [0.1, 0.68, 0.22], [0.22, 0.1, 0.68]] <> fromLists [[0.2], [0.5], [0.3]](3><1) [0.276, 0.426, 0.298] ENow if we were given just the convolution and the output, we can use  to infer the input. clinear 3.0e-17 $ LC [[0.68, 0.22, 0.1], [0.1, 0.68, 0.22], [0.22, 0.1, 0.68]] [0.276, 0.426, 0.298]2Right [0.20000000000000004,0.4999999999999999,0.3]WI fell compelled to point out that we could also just invert the original convolution T matrix. Supposedly using maxent can reduce errors from noise if the convolution % matrix is not properly estimated. #Tolerance for the numerical solver !The matrix A and column vector b HEither the a discription of what wrong or the probability distribution !The matrix A and column vector b GEither the a discription of what wrong or the probability distribution !The matrix A and column vector b GEither the a discription of what wrong or the probability distribution    None None           ! "#$%maxent-0.6.0.4Numeric.MaxEntNumeric.MaxEnt.General Numeric.MaxEnt.ConjugateGradientNumeric.MaxEnt.MomentNumeric.MaxEnt.LinearNumeric.MaxEnt.Internallagrangian-0.5.0.0Numeric.AD.Lagrangian.Internal ConstraintgeneralUUunUUExpectationFunctionExpectationConstraint.=.averagevariancemaxentLinearConstraintsLCmatrixoutputlinearentropydotsumMapsumWithminimizeprobs partialPart partitionFunc objectiveFuncmultMVlinear'linear''test1