In　this　letter,　we　consider　the　robust　finite-horizon　filtering　problem　for　a　class　of　discrete　time-varying　systems　with　missing　measurements　and　norm-bounded　parameter　uncertainties.　The　missing　measurements　are　described　by　a　binary　switching　sequence　satisfying　a　conditional　probability　distribution.　An　upper　bound　for　the　state　estimation　error　variance　is　first　derived　for　all　possible　missing　observations　and　all　admissible　parameter　uncertainties.　Then,　a　robust　filter　is　designed,　guaranteeing　that　the　variance　of　the　state　estimation　error　is　not　more　than　the　prescribed　upper　bound.　It　is　shown　that　the　desired　filter　can　be　obtained　in　terms　of　the　solutions　to　two　discrete　Riccati　difference　equations,　which　are　of　a　form　suitable　for　recursive　computation　in　online　applications.　A　simulation　example　is　presented　to　show　the　effectiveness　of　the　proposed　approach　by　comparing　to　the　traditional　Kalman　filtering　method.