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This paper presents an empirical cost-bene¯t analysis of an algorithm called Distribution Estimation Using MRF with direct sampling (DEUMd). DEUMd belongs to the family of Estimation of Distribution Algorithm (EDA). Particu-larly it is a univariate EDA. DEUMd uses a computation-ally more expensive model to estimate the probability dis-tribution than other univariate EDAs. We investigate the performance of DEUMd in a range of optimization problem. Our experiments shows a better performance (in terms of the number of ¯tness evaluation needed by the algorithm to ¯nd a solution and the quality of the solution) of DEUMd on most of the problems analysed in this paper in comparison to that of other univariate EDAs. We conclude that use of a Markov Network in a univariate EDA can be of net bene¯t in de¯ned set of circumstances. Categories and Subject Descriptor