In sugarcane or other autopolyploids, after generating the data, the first step in constructing molecular marker maps is to determine marker dosage. Improved methods for correctly allocating marker dosage will result in more accurate maps and increased efficiency of QTL linkage detection. When employing dominant markers like AFLPs, single-dose markers represent alleles present as one copy in one parent and null in the other parent, double-dose markers are those present as two copies in one parent and null in the other parent and so on. Observed segregation ratios in the offspring are employed to infer marker dosage in the parent from which the marker was inherited. Commonly, for each marker, a chi (2) test is used to assign dosage. Such an approach does not address important practical considerations such as multiple testing and departures from theoretical assumptions. In particular, extra-binomial variation or overdispersion has been observed in sugarcane studies and standard methods may result in fewer correct dosage allocations than the data warrant. To address these shortcomings, a Bayesian mixture model is proposed where all markers are considered simultaneously. Since analytic solutions are not available, Markov chain Monte Carlo methods are employed. Marker dosage allocation for each individual marker employs the estimated posterior probability of each dosage. For a sugarcane study these methods resulted in more markers being allocated a dosage than by standard approaches. Simulation studies demonstrated that, in general, not only are more markers classified but that more markers are also correctly classified, particularly if overdispersion is present.