This　paper　studies　the　problem　of　simultaneous　deciding　on　hypotheses　and　estimating　a　random　parameter.　We　propose　a　joint　decision　and　estimation　(JDE)　formulation,　which　amounts　to　minimizing　a　risk　related　to　both　decision　and　estimation　while　decision　performance　is　also　constrained　within　a　tolerable　level.　The　risk　used　in　this　paper　is　a　weighted　sum　of　estimation　costs　conditioned　on　correct　decisions　times　the　correct　decision　probabilities.　Such　a　risk　allows　us　to　focus　on　the　joint　(decision　and　estimation)　performance　pertinent　to　correct　decisions　only.　The　JDE　solution　includes　handling　two　subproblems　-　estimation　and　decision.　For　the　estimation　subproblem,　we　derive　the　optimal　Bayes　estimator.　For　the　decision　subproblem,　we　provide　the　optimal　solution　via　a　numerical　algorithm　of　a　two-dimensional　search.　To　reduce　the　complexity,　we　also　provide　a　suboptimal　solution　when　the　decision　rule　is　confined　to　the　likelihood　ratio　tests.　The　suboptimal　solution　is　implemented　with　a　one-dimensional　search　in　a　finite　range.　Numerical　results　are　provided　to　validate　our　methods