Dioxin　(DXN),　a　by-product　from　the　municipal　solid　waste　incineration　(MSWI)　process,　is　an　organic　pollutant;　thus,　it　is　extremely　harmful　to　the　ecological　environment　and　difficult　to　detect　in　real-time.　A　selective　ensemble　(SEN)　model　for　DXN　emission　concentration　based　on　Bayesian　inference　and　binary　trees　is　proposed　given　the　weak　interpretability,　high　model　complexity,　and　poor　generalization　performance　of　the　existing　DXN　emission　concentration　prediction　model.　Initially,　bagging　sampling　is　used　to　obtain　different　data　subsets.　The　binary　tree,　as　the　candidate　submodel,　is　constructed　based　on　the　subdatasets,　and　the　prior　information　of　the　leaf　nodes　and　predicted　values　of　the　candidate　submodel　are　calculated.　Bayesian　inference　is　used　to　calculate　the　posterior　information　to　characterize　the　fitness　of　the　candidate　submodel.　On　the　basis　of　the　posterior　error,　the　best　submodel　is　selected　as　the　ensemble　submodel.　These　processes　are　repeated　to　obtain　all　the　ensembled　submodels　and　the　corresponding　posterior　information.　Then,　the　combined　weights　are　determined　by　the　posterior　information　of　all　ensemble　submodels,　and　the　DXN　emission　concentration　SEN　model　is　constructed.　The　effectiveness　of　the　proposed　method　is　verified　using　the　actual　data　of　the　MSWI　process.　IEEE