úÎ$¸#k     NoneHM   None*3HM'This is for the linear case Ax = b x1 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.flinear 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])MRight (fromList [0.2000000000000001,0.49999999999999983,0.30000000000000004])ÍI fell compelled to point out that we could also just invert the original convolution 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 bKEither a description of what went wrong or the probability distribution  The matrix A and column vector bJEither a description of what went wrong or the probability distribution The matrix A and column vector bJEither a description of what went wrong or the probability distribution NoneEHM™A more general solver. This directly solves the lagrangian of the constraints and the the additional constraint that the probabilities must sum to one."Tolerance for the numerical solverthe count of probabilities constraintsFEither the a discription of what wrong or the probability distributionNoneHM7Constraint 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 overThe constraintsGEither the a discription of what wrong or the probability distribution     None None         !" maxent-0.7Numeric.MaxEnt Numeric.MaxEnt.ConjugateGradientNumeric.MaxEnt.LinearNumeric.MaxEnt.GeneralNumeric.MaxEnt.MomentNumeric.MaxEnt.Internallagrangian-0.6.0.1Numeric.AD.Lagrangian.Internal ConstraintLinearConstraintsLCunLClinearlinear'linear''generalExpectationConstraint.=.averagevariancemaxentdotsumMapsumWithminimizemultMVprobs partitionFunc objectiveFuncentropyExpConunExpCon expCon2Con