Applying　Bayesian　approach　to　decision　tree　(DT)　model,　and　then　a　Bayesian-inference-based　decision　tree　(BDT)　model　is　proposed.　For　BDT　we　assign　prior　to　the　model　parameters.　Together　with　observed　samples,　prior　are　converted　to　posterior　through　Bayesian　inference.　When　making　inference　we　resort　to　simulation　methods　using　reversible　jump　Markov　chain　Monte　Carlo　(RJMCMC)　since　the　dimension　of　posterior　distribution　is　varying.　Compared　with　DT,　BDT　enjoys　the　following　three　advantages.　Firstly,　the　model＇s　learning　procedure　is　implemented　with　sampling　instead　of　a　series　of　splitting　and　pruning　operations.　Secondly,　the　model　provides　output　that　gives　insight　into　different　tree　structures　and　recursive　partition　of　the　decision　space,　resulting　in　better　classification　accuracy.　And　thirdly,　the　model　can　indicate　confidence　that　the　sample　belongs　to　a　particular　class　in　classification.　The　experiments　on　music　style　classification　demonstrate　the　efficiency　of　BDT.