Genomewide analysis of linkage disequilibrium (LD) is commonly based in the maximum likelihood estimator. This estimator of LD suffers of a well known bias toward disequilibrium that becomes particularly serious in small samples with SNPs that are not very common in the population. Algorithms able to identify LD patterns, such as haplotype blocks or LD decay maps do a non-random selection of SNPs to be included in the analysis in order to remove this bias. However, they introduce ascertainment bias that can mask the real decay of disequilibrium in the population, with several consequences on the validity and reproducibility of genetic studies. In this work, we use a new Bayesian estimator of LD that greatly reduces the effect of ascertainment bias in the inference of LD decay. We also provide a software that use the Bayesian estimator to compute pairwise LD from SNP samples.