{
 "case": "lml",
 "tag": "v4.35.0",
 "pass": "raw",
 "build_rc": 1,
 "cache_rc": 0,
 "cache_s": 48.5,
 "build_s": 50.6,
 "errors": 30,
 "files_with_errors": 3,
 "deprecation_warnings": 3,
 "errors_detail": [
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/WithDensity.lean",
   "line": 68,
   "col": 6,
   "msg": "Unknown identifier `compProd_withDensity`",
   "detail": [],
   "kind": "other",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/WithDensity.lean",
   "line": 67,
   "col": 62,
   "msg": "unsolved goals",
   "detail": [
    "\u03b1 : Type u_1",
    "\u03b2 : Type u_2",
    "m\u03b1 : MeasurableSpace \u03b1",
    "m\u03b2 : MeasurableSpace \u03b2",
    "\u03bc : Measure \u03b1",
    "inst\u271d\u00b2 : SFinite \u03bc",
    "\u03ba : Kernel \u03b1 \u03b2",
    "inst\u271d\u00b9 : IsSFiniteKernel \u03ba",
    "f : \u03b1 \u2192 \u211d\u22650\u221e",
    "g : \u03b1 \u2192 \u03b2 \u2192 \u211d\u22650\u221e",
    "hf : Measurable f",
    "hg : Measurable (Function.uncurry g)",
    "inst\u271d : IsSFiniteKernel (\u03ba.withDensity g)",
    "\u22a2 \u03bc.withDensity f \u2297\u2098 \u03ba.withDensity g = (\u03bc \u2297\u2098 \u03ba).withDensity fun ac \u21a6 f ac.1 * g ac.1 ac.2"
   ],
   "kind": "tactic-failed",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/IndepFun.lean",
   "line": 106,
   "col": 10,
   "msg": "Unknown constant `MeasureTheory.Measure.isProbabilityMeasure_map`",
   "detail": [],
   "kind": "other",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/IndepFun.lean",
   "line": 105,
   "col": 61,
   "msg": "unsolved goals",
   "detail": [
    "\u03a9 : Type u_2",
    "\u03a9' : Type u_3",
    "E : Type u_4",
    "\u03b9 : Type u_5",
    "inst\u271d\u00b9 : Countable \u03b9",
    "m\u03a9 : MeasurableSpace \u03a9",
    "m\u03a9' : MeasurableSpace \u03a9'",
    "mE : MeasurableSpace E",
    "\u03bc : Measure \u03a9",
    "inst\u271d : IsProbabilityMeasure \u03bc",
    "X : \u03b9 \u2192 \u03a9' \u2192 E",
    "f : \u03a9 \u2192 \u03a9'",
    "hf : AEMeasurable f \u03bc",
    "hX : \u2200 (n : \u03b9), AEMeasurable (X n) (Measure.map f \u03bc)",
    "\u22a2 iIndepFun X (Measure.map f \u03bc) \u2194 iIndepFun (fun n \u21a6 X n \u2218 f) \u03bc"
   ],
   "kind": "tactic-failed",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 39,
   "col": 8,
   "msg": "Tactic `rewrite` failed: Did not find an occurrence of the pattern",
   "detail": [
    "  Measure.map Y \u03bc \u2297\u2098 condDistrib ?m.67 Y \u03bc",
    "in the target expression",
    "  Measure.map Prod.swap (Measure.map X \u03bc \u2297\u2098 condDistrib Y X \u03bc) = Measure.map (fun a \u21a6 (Y a, X a)) \u03bc",
    "",
    "case pos",
    "\u03b1 : Type u_1",
    "\u03b2 : Type u_2",
    "\u03a9 : Type u_5",
    "m\u03b1 : MeasurableSpace \u03b1",
    "\u03bc : Measure \u03b1",
    "m\u03b2 : MeasurableSpace \u03b2",
    "inst\u271d\u00b3 : MeasurableSpace \u03a9",
    "inst\u271d\u00b2 : StandardBorelSpace \u03a9",
    "inst\u271d\u00b9 : Nonempty \u03a9",
    "X : \u03b1 \u2192 \u03b2",
    "Y : \u03b1 \u2192 \u03a9",
    "inst\u271d : IsFiniteMeasure \u03bc",
    "hY : AEMeasurable Y \u03bc",
    "hX : AEMeasurable X \u03bc",
    "\u22a2 Measure.map Prod.swap (Measure.map X \u03bc \u2297\u2098 condDistrib Y X \u03bc) = Measure.map (fun a \u21a6 (Y a, X a)) \u03bc"
   ],
   "kind": "tactic-failed",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 44,
   "col": 4,
   "msg": "`simp` made no progress",
   "detail": [],
   "kind": "other",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 50,
   "col": 59,
   "msg": "Application type mismatch: The argument",
   "detail": [
    "  T",
    "has type",
    "  \u03b1 \u2192 \u03b3",
    "of sort `Type (max u_1 u_3)` but is expected to have type",
    "  AEMeasurable ?m.82 \u03bc",
    "of sort `Prop` in the application",
    "  condDistrib_ae_eq_of_measure_eq_compProd T"
   ],
   "kind": "type-mismatch",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 51,
   "col": 33,
   "msg": "Tactic `rewrite` failed: Did not find an occurrence of the pattern",
   "detail": [
    "  Measure.map X \u03bc \u2297\u2098 condDistrib ?m.120 X \u03bc",
    "in the target expression",
    "  Measure.map (fun x \u21a6 (T x, X x, Y x)) \u03bc =",
    "    Measure.map (\u21d1MeasurableEquiv.prodAssoc)",
    "      (Measure.map T \u03bc \u2297\u2098 condDistrib X T \u03bc \u2297\u2098 condDistrib Y (fun \u03c9 \u21a6 (T \u03c9, X \u03c9)) \u03bc)",
    "",
    "\u03b1 : Type u_1",
    "\u03b2 : Type u_2",
    "\u03b3 : Type u_3",
    "\u03a9 : Type u_5",
    "m\u03b1 : MeasurableSpace \u03b1",
    "\u03bc : Measure \u03b1",
    "m\u03b2 : MeasurableSpace \u03b2",
    "m\u03b3 : MeasurableSpace \u03b3",
    "inst\u271d\u2075 : MeasurableSpace \u03a9",
    "inst\u271d\u2074 : StandardBorelSpace \u03a9",
    "inst\u271d\u00b3 : Nonempty \u03a9",
    "X : \u03b1 \u2192 \u03b2",
    "Y : \u03b1 \u2192 \u03a9",
    "T : \u03b1 \u2192 \u03b3",
    "inst\u271d\u00b2 : IsFiniteMeasure \u03bc",
    "inst\u271d\u00b9 : StandardBorelSpace \u03b2",
    "inst\u271d : Nonempty \u03b2",
    "hX : AEMeasurable X \u03bc",
    "hY : AEMeasurable Y \u03bc",
    "hT : AEMeasurable T \u03bc",
    "\u22a2 Measure.map (fun x \u21a6 (T x, X x, Y x)) \u03bc =",
    "    Measure.map (\u21d1MeasurableEquiv.prodAssoc)",
    "      (Measure.map T \u03bc \u2297\u2098 condDistrib X T \u03bc \u2297\u2098 condDistrib Y (fun \u03c9 \u21a6 (T \u03c9, X \u03c9)) \u03bc)"
   ],
   "kind": "tactic-failed",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 63,
   "col": 59,
   "msg": "Application type mismatch: The argument",
   "detail": [
    "  T",
    "has type",
    "  \u03b1 \u2192 \u03b3",
    "of sort `Type (max u_1 u_3)` but is expected to have type",
    "  AEMeasurable ?m.118 ?m.116",
    "of sort `Prop` in the application",
    "  condDistrib_comp T"
   ],
   "kind": "type-mismatch",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 63,
   "col": 21,
   "msg": "Application type mismatch: The argument",
   "detail": [
    "  T",
    "has type",
    "  \u03b1 \u2192 \u03b3",
    "of sort `Type (max u_1 u_3)` but is expected to have type",
    "  AEMeasurable ?m.142 ?m.140",
    "of sort `Prop` in the application",
    "  condDistrib_comp T"
   ],
   "kind": "type-mismatch",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 64,
   "col": 48,
   "msg": "Application type mismatch: The argument",
   "detail": [
    "  f",
    "has type",
    "  \u03a9' \u2192 \u03b2",
    "of sort `Type (max u_2 u_6)` but is expected to have type",
    "  AEMeasurable ?m.222 ?m.220",
    "of sort `Prop` in the application",
    "  condDistrib_ae_eq_iff_measure_eq_compProd f"
   ],
   "kind": "type-mismatch",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 60,
   "col": 70,
   "msg": "unsolved goals",
   "detail": [
    "\u03b1 : Type u_1",
    "\u03b2 : Type u_2",
    "\u03b3 : Type u_3",
    "\u03a9 : Type u_5",
    "\u03a9' : Type u_6",
    "m\u03b1 : MeasurableSpace \u03b1",
    "\u03bc : Measure \u03b1",
    "m\u03b2 : MeasurableSpace \u03b2",
    "m\u03b3 : MeasurableSpace \u03b3",
    "inst\u271d\u2077 : MeasurableSpace \u03a9",
    "inst\u271d\u2076 : StandardBorelSpace \u03a9",
    "inst\u271d\u2075 : Nonempty \u03a9",
    "m\u03a9' : MeasurableSpace \u03a9'",
    "inst\u271d\u2074 : StandardBorelSpace \u03a9'",
    "inst\u271d\u00b3 : Nonempty \u03a9'",
    "Z : \u03b1 \u2192 \u03a9'",
    "T : \u03b1 \u2192 \u03b3",
    "inst\u271d\u00b2 : IsFiniteMeasure \u03bc",
    "inst\u271d\u00b9 : StandardBorelSpace \u03b2",
    "inst\u271d : Nonempty \u03b2",
    "f : \u03a9' \u2192 \u03b2",
    "g : \u03a9' \u2192 \u03a9",
    "hf : Measurable f",
    "hg : Measurable g",
    "hZ : AEMeasurable Z \u03bc",
    "hT : AEMeasurable T \u03bc",
    "t : \u03b3",
    "h_prod :",
    "  (condDistrib (fun \u03c9 \u21a6 ((f \u2218 Z) \u03c9, (g \u2218 Z) \u03c9)) T \u03bc) t =",
    "    (condDistrib (f \u2218 Z) T \u03bc \u2297\u2096 condDistrib (g \u2218 Z) (fun \u03c9 \u21a6 (T \u03c9, (f \u2218 Z) \u03c9)) \u03bc) t",
    "h_pair : (condDistrib ((fun a \u21a6 (f a, g a)) \u2218 Z) T \u03bc) t = ((condDistrib Z T \u03bc).map fun a \u21a6 (f a, g a)) t",
    "h_fst : (condDistrib (f \u2218 Z) T \u03bc) t = ((condDistrib Z T \u03bc).map f) t",
    "\u22a2 \u21d1(condDistrib g f ((condDistrib Z T \u03bc) t)) =\u1d50[Measure.map f ((condDistrib Z T \u03bc) t)]",
    "    \u21d1((condDistrib (g \u2218 Z) (fun a \u21a6 (T a, (f \u2218 Z) a)) \u03bc).sectR t)"
   ],
   "kind": "tactic-failed",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 78,
   "col": 47,
   "msg": "Application type mismatch: The argument",
   "detail": [
    "  fun \u03c9 \u21a6 (T \u03c9, X \u03c9)",
    "has type",
    "  \u03b1 \u2192 \u03b3 \u00d7 \u03b2",
    "of sort `Type (max (max u_1 u_2) u_3)` but is expected to have type",
    "  AEMeasurable ?m.89 \u03bc",
    "of sort `Prop` in the application",
    "  @condDistrib_comp_self \u03b1 ?m.82 ?m.83 ?m.84 ?m.85 ?m.86 m\u03b1 \u03bc ?m.88 ?m.89 ?m.90 fun \u03c9 \u21a6 (T \u03c9, X \u03c9)"
   ],
   "kind": "type-mismatch",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 78,
   "col": 88,
   "msg": "`fun_prop` was unable to prove `Measurable Prod.fst`",
   "detail": [
    "",
    "Issues:",
    "  Failed to infer `(?m.94 : Type ?u.94)` when applying theorem `Measurable.fst`."
   ],
   "kind": "other",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 76,
   "col": 87,
   "msg": "unsolved goals",
   "detail": [
    "\u03b1 : Type u_1",
    "\u03b2 : Type u_2",
    "\u03b3 : Type u_3",
    "m\u03b1 : MeasurableSpace \u03b1",
    "\u03bc : Measure \u03b1",
    "m\u03b2 : MeasurableSpace \u03b2",
    "m\u03b3 : MeasurableSpace \u03b3",
    "X : \u03b1 \u2192 \u03b2",
    "T : \u03b1 \u2192 \u03b3",
    "inst\u271d\u2074 : IsFiniteMeasure \u03bc",
    "inst\u271d\u00b3 : StandardBorelSpace \u03b2",
    "inst\u271d\u00b2 : Nonempty \u03b2",
    "inst\u271d\u00b9 : StandardBorelSpace \u03b3",
    "inst\u271d : Nonempty \u03b3",
    "hX : AEMeasurable X \u03bc",
    "hT : AEMeasurable T \u03bc",
    "h_prod :",
    "  \u21d1(condDistrib (fun \u03c9 \u21a6 (X \u03c9, T \u03c9)) T \u03bc) =\u1d50[Measure.map T \u03bc]",
    "    \u21d1(condDistrib X T \u03bc \u2297\u2096 condDistrib T (fun \u03c9 \u21a6 (T \u03c9, X \u03c9)) \u03bc)",
    "\u22a2 \u21d1(condDistrib (fun \u03c9 \u21a6 (X \u03c9, T \u03c9)) T \u03bc) =\u1d50[Measure.map T \u03bc] \u21d1(condDistrib X T \u03bc \u00d7\u2096 Kernel.id)"
   ],
   "kind": "tactic-failed",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 105,
   "col": 43,
   "msg": "unsolved goals",
   "detail": [
    "\u03b1 : Type u_1",
    "\u03b2 : Type u_2",
    "\u03b3 : Type u_3",
    "\u03b4 : Type u_4",
    "m\u03b1 : MeasurableSpace \u03b1",
    "\u03bc : Measure \u03b1",
    "m\u03b2 : MeasurableSpace \u03b2",
    "m\u03b3 : MeasurableSpace \u03b3",
    "m\u03b4 : MeasurableSpace \u03b4",
    "inst\u271d\u2077 : IsFiniteMeasure \u03bc",
    "inst\u271d\u2076 : StandardBorelSpace \u03b1",
    "inst\u271d\u2075 : StandardBorelSpace \u03b2",
    "inst\u271d\u2074 : Nonempty \u03b2",
    "inst\u271d\u00b3 : StandardBorelSpace \u03b3",
    "inst\u271d\u00b2 : Nonempty \u03b3",
    "inst\u271d\u00b9 : StandardBorelSpace \u03b4",
    "inst\u271d : Nonempty \u03b4",
    "X : \u03b1 \u2192 \u03b2",
    "Y : \u03b1 \u2192 \u03b3",
    "Z : \u03b1 \u2192 \u03b4",
    "hX : Measurable X",
    "hY : Measurable Y",
    "hZ : Measurable Z",
    "h :",
    "  Measure.map (fun x \u21a6 ((Z x, X x), Y x)) \u03bc =",
    "    Measure.map (fun \u03c9 \u21a6 (Z \u03c9, X \u03c9)) \u03bc \u2297\u2098 Kernel.prodMkRight \u03b2 (condDistrib Y Z \u03bc)",
    "\u22a2 AEMeasurable (fun \u03c9 \u21a6 (Z \u03c9, X \u03c9)) \u03bc",
    "",
    "\u03b1 : Type u_1",
    "\u03b2 : Type u_2",
    "\u03b3 : Type u_3",
    "\u03b4 : Type u_4",
    "m\u03b1 : MeasurableSpace \u03b1",
    "\u03bc : Measure \u03b1",
    "m\u03b2 : MeasurableSpace \u03b2",
    "m\u03b3 : MeasurableSpace \u03b3",
    "m\u03b4 : MeasurableSpace \u03b4",
    "inst\u271d\u2077 : IsFiniteMeasure \u03bc",
    "inst\u271d\u2076 : StandardBorelSpace \u03b1",
    "inst\u271d\u2075 : StandardBorelSpace \u03b2"
   ],
   "kind": "tactic-failed",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 152,
   "col": 59,
   "msg": "Application type mismatch: The argument",
   "detail": [
    "  X",
    "has type",
    "  \u03b1 \u2192 \u03b2",
    "of sort `Type (max u_1 u_2)` but is expected to have type",
    "  AEMeasurable ?m.58 \u03bc",
    "of sort `Prop` in the application",
    "  condDistrib_ae_eq_of_measure_eq_compProd X"
   ],
   "kind": "type-mismatch",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 159,
   "col": 51,
   "msg": "Tactic `rewrite` failed: Did not find an occurrence of the pattern",
   "detail": [
    "  Measure.map (fun a \u21a6 (Y a, ?m.89 a)) \u03bc",
    "in the target expression",
    "  Measure.map (fun \u03c9 \u21a6 (X \u03c9, Y \u03c9)) \u03bc = (Measure.map X \u03bc).prod (Measure.map Y \u03bc)",
    "",
    "\u03b1 : Type u_1",
    "\u03b2 : Type u_2",
    "\u03a9 : Type u_5",
    "m\u03b1 : MeasurableSpace \u03b1",
    "\u03bc : Measure \u03b1",
    "m\u03b2 : MeasurableSpace \u03b2",
    "inst\u271d\u00b3 : MeasurableSpace \u03a9",
    "inst\u271d\u00b2 : StandardBorelSpace \u03a9",
    "inst\u271d\u00b9 : Nonempty \u03a9",
    "X : \u03b1 \u2192 \u03b2",
    "Y : \u03b1 \u2192 \u03a9",
    "inst\u271d : IsFiniteMeasure \u03bc",
    "hX : AEMeasurable X \u03bc",
    "hY : AEMeasurable Y \u03bc",
    "h : \u21d1(condDistrib Y X \u03bc) =\u1d50[Measure.map X \u03bc] \u21d1(Kernel.const \u03b2 (Measure.map Y \u03bc))",
    "\u22a2 Measure.map (fun \u03c9 \u21a6 (X \u03c9, Y \u03c9)) \u03bc = (Measure.map X \u03bc).prod (Measure.map Y \u03bc)"
   ],
   "kind": "tactic-failed",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 280,
   "col": 10,
   "msg": "Tactic `rewrite` failed: Did not find an occurrence of the pattern",
   "detail": [
    "  (Measure.map (fun a \u21a6 (X a, ?m.186 a)) ?m.187).fst",
    "in the target expression",
    "  Measure.map Z \u03bc = (Measure.map (fun \u03c9 \u21a6 (Z \u03c9, X \u03c9)) \u03bc).fst",
    "",
    "\u03b1 : Type u_1",
    "\u03b2 : Type u_2",
    "\u03a9 : Type u_5",
    "\u03a9' : Type u_6",
    "m\u03b1 : MeasurableSpace \u03b1",
    "\u03bc : Measure \u03b1",
    "m\u03b2 : MeasurableSpace \u03b2",
    "inst\u271d\u2077 : MeasurableSpace \u03a9",
    "inst\u271d\u2076 : StandardBorelSpace \u03a9",
    "inst\u271d\u2075 : Nonempty \u03a9",
    "m\u03a9' : MeasurableSpace \u03a9'",
    "X : \u03b1 \u2192 \u03b2",
    "Y : \u03b1 \u2192 \u03a9",
    "Z : \u03b1 \u2192 \u03a9'",
    "inst\u271d\u2074 : IsFiniteMeasure \u03bc",
    "inst\u271d\u00b3 : StandardBorelSpace \u03b1",
    "inst\u271d\u00b2 : StandardBorelSpace \u03b2",
    "inst\u271d\u00b9 : Nonempty \u03b2",
    "hX : Measurable X",
    "hY : Measurable Y",
    "hZ : Measurable Z",
    "\u03b7 : Kernel \u03a9' \u03a9",
    "inst\u271d : IsMarkovKernel \u03b7",
    "h : Measure.map (fun x \u21a6 ((Z x, X x), Y x)) \u03bc = Measure.map (fun \u03c9 \u21a6 (Z \u03c9, X \u03c9)) \u03bc \u2297\u2098 Kernel.prodMkRight \u03b2 \u03b7",
    "\u22a2 Measure.map Z \u03bc = (Measure.map (fun \u03c9 \u21a6 (Z \u03c9, X \u03c9)) \u03bc).fst"
   ],
   "kind": "tactic-failed",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 277,
   "col": 50,
   "msg": "unsolved goals",
   "detail": [
    "\u03b1 : Type u_1",
    "\u03b2 : Type u_2",
    "\u03a9 : Type u_5",
    "\u03a9' : Type u_6",
    "m\u03b1 : MeasurableSpace \u03b1",
    "\u03bc : Measure \u03b1",
    "m\u03b2 : MeasurableSpace \u03b2",
    "inst\u271d\u2077 : MeasurableSpace \u03a9",
    "inst\u271d\u2076 : StandardBorelSpace \u03a9",
    "inst\u271d\u2075 : Nonempty \u03a9",
    "m\u03a9' : MeasurableSpace \u03a9'",
    "X : \u03b1 \u2192 \u03b2",
    "Y : \u03b1 \u2192 \u03a9",
    "Z : \u03b1 \u2192 \u03a9'",
    "inst\u271d\u2074 : IsFiniteMeasure \u03bc",
    "inst\u271d\u00b3 : StandardBorelSpace \u03b1",
    "inst\u271d\u00b2 : StandardBorelSpace \u03b2",
    "inst\u271d\u00b9 : Nonempty \u03b2",
    "hX : Measurable X",
    "hY : Measurable Y",
    "hZ : Measurable Z",
    "\u03b7 : Kernel \u03a9' \u03a9",
    "inst\u271d : IsMarkovKernel \u03b7",
    "h : Measure.map (fun x \u21a6 ((Z x, X x), Y x)) \u03bc = Measure.map (fun \u03c9 \u21a6 (Z \u03c9, X \u03c9)) \u03bc \u2297\u2098 Kernel.prodMkRight \u03b2 \u03b7",
    "\u22a2 AEMeasurable Z \u03bc",
    "",
    "\u03b1 : Type u_1",
    "\u03b2 : Type u_2",
    "\u03a9 : Type u_5",
    "\u03a9' : Type u_6",
    "m\u03b1 : MeasurableSpace \u03b1",
    "\u03bc : Measure \u03b1",
    "m\u03b2 : MeasurableSpace \u03b2",
    "inst\u271d\u2077 : MeasurableSpace \u03a9",
    "inst\u271d\u2076 : StandardBorelSpace \u03a9",
    "inst\u271d\u2075 : Nonempty \u03a9",
    "m\u03a9' : MeasurableSpace \u03a9'",
    "X : \u03b1 \u2192 \u03b2",
    "Y : \u03b1 \u2192 \u03a9",
    "Z : \u03b1 \u2192 \u03a9'"
   ],
   "kind": "tactic-failed",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 276,
   "col": 24,
   "msg": "unsolved goals",
   "detail": [
    "\u03b1 : Type u_1",
    "\u03b2 : Type u_2",
    "\u03a9 : Type u_5",
    "\u03a9' : Type u_6",
    "m\u03b1 : MeasurableSpace \u03b1",
    "\u03bc : Measure \u03b1",
    "m\u03b2 : MeasurableSpace \u03b2",
    "inst\u271d\u2077 : MeasurableSpace \u03a9",
    "inst\u271d\u2076 : StandardBorelSpace \u03a9",
    "inst\u271d\u2075 : Nonempty \u03a9",
    "m\u03a9' : MeasurableSpace \u03a9'",
    "X : \u03b1 \u2192 \u03b2",
    "Y : \u03b1 \u2192 \u03a9",
    "Z : \u03b1 \u2192 \u03a9'",
    "inst\u271d\u2074 : IsFiniteMeasure \u03bc",
    "inst\u271d\u00b3 : StandardBorelSpace \u03b1",
    "inst\u271d\u00b2 : StandardBorelSpace \u03b2",
    "inst\u271d\u00b9 : Nonempty \u03b2",
    "hX : Measurable X",
    "hY : Measurable Y",
    "hZ : Measurable Z",
    "\u03b7 : Kernel \u03a9' \u03a9",
    "inst\u271d : IsMarkovKernel \u03b7",
    "h : \u21d1(condDistrib Y (fun \u03c9 \u21a6 (Z \u03c9, X \u03c9)) \u03bc) =\u1d50[Measure.map (fun \u03c9 \u21a6 (Z \u03c9, X \u03c9)) \u03bc] \u21d1(Kernel.prodMkRight \u03b2 \u03b7)",
    "h\u03b7_eq : \u21d1(condDistrib Y Z \u03bc) =\u1d50[Measure.map Z \u03bc] \u21d1\u03b7",
    "\u22a2 Measurable X"
   ],
   "kind": "tactic-failed",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 300,
   "col": 8,
   "msg": "Unknown constant `ProbabilityTheory.Kernel.compProd_eq_iff`",
   "detail": [],
   "kind": "other",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 298,
   "col": 24,
   "msg": "unsolved goals",
   "detail": [
    "\u03b1 : Type u_1",
    "\u03b2 : Type u_2",
    "\u03a9 : Type u_5",
    "\u03a9' : Type u_6",
    "m\u03b1 : MeasurableSpace \u03b1",
    "\u03bc : Measure \u03b1",
    "m\u03b2 : MeasurableSpace \u03b2",
    "inst\u271d\u2077 : MeasurableSpace \u03a9",
    "inst\u271d\u2076 : StandardBorelSpace \u03a9",
    "inst\u271d\u2075 : Nonempty \u03a9",
    "m\u03a9' : MeasurableSpace \u03a9'",
    "X : \u03b1 \u2192 \u03b2",
    "Y : \u03b1 \u2192 \u03a9",
    "Z : \u03b1 \u2192 \u03a9'",
    "inst\u271d\u2074 : IsFiniteMeasure \u03bc",
    "inst\u271d\u00b3 : StandardBorelSpace \u03b1",
    "inst\u271d\u00b2 : StandardBorelSpace \u03b2",
    "inst\u271d\u00b9 : Nonempty \u03b2",
    "hX : Measurable X",
    "hY : Measurable Y",
    "hZ : Measurable Z",
    "\u03b7 : Kernel \u03a9' \u03a9",
    "inst\u271d : IsMarkovKernel \u03b7",
    "h : \u21d1(condDistrib Y (fun \u03c9 \u21a6 (X \u03c9, Z \u03c9)) \u03bc) =\u1d50[Measure.map (fun \u03c9 \u21a6 (X \u03c9, Z \u03c9)) \u03bc] \u21d1(Kernel.prodMkLeft \u03b2 \u03b7)",
    "\u22a2 \u21d1(condDistrib Y (fun \u03c9 \u21a6 (Z \u03c9, X \u03c9)) \u03bc) =\u1d50[Measure.map (fun \u03c9 \u21a6 (Z \u03c9, X \u03c9)) \u03bc] \u21d1(Kernel.prodMkRight \u03b2 \u03b7)"
   ],
   "kind": "tactic-failed",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 336,
   "col": 4,
   "msg": "Type mismatch",
   "detail": [
    "  fun hY \u21a6 condDistrib_fst_prod ?m.253 hY \u03bd",
    "has type",
    "  AEMeasurable Y \u03bc \u2192 \u21d1\ud835\udcdb[fun \u03c9 \u21a6 Y \u03c9.1 | fun \u03c9 \u21a6 Z \u03c9.1; \u03bc.prod \u03bd] =\u1d50[Measure.map Z \u03bc] \u21d1\ud835\udcdb[Y | Z; \u03bc]",
    "but is expected to have type",
    "  \u21d1\ud835\udcdb[fun \u03c9 \u21a6 Y \u03c9.1 | fun \u03c9 \u21a6 Z \u03c9.1; \u03bc.prod \u03bd] =\u1d50[Measure.map Z \u03bc] \u21d1\ud835\udcdb[Y | Z; \u03bc]"
   ],
   "kind": "other",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 338,
   "col": 4,
   "msg": "Type mismatch",
   "detail": [
    "  fun hY \u21a6 condDistrib_fst_prod ?m.321 hY \u03bd",
    "has type",
    "  AEMeasurable X \u03bc \u2192 \u21d1\ud835\udcdb[fun \u03c9 \u21a6 X \u03c9.1 | fun \u03c9 \u21a6 Z \u03c9.1; \u03bc.prod \u03bd] =\u1d50[Measure.map Z \u03bc] \u21d1\ud835\udcdb[X | Z; \u03bc]",
    "but is expected to have type",
    "  \u21d1\ud835\udcdb[fun \u03c9 \u21a6 X \u03c9.1 | fun \u03c9 \u21a6 Z \u03c9.1; \u03bc.prod \u03bd] =\u1d50[Measure.map Z \u03bc] \u21d1\ud835\udcdb[X | Z; \u03bc]"
   ],
   "kind": "other",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 420,
   "col": 45,
   "msg": "Application type mismatch: The argument",
   "detail": [
    "  X",
    "has type",
    "  \u03b1 \u2192 \u03b2",
    "of sort `Type (max u_1 u_2)` but is expected to have type",
    "  AEMeasurable ?m.64 \u03bc",
    "of sort `Prop` in the application",
    "  @condDistrib_comp_self \u03b1 ?m.57 ?m.58 ?m.59 ?m.60 ?m.61 m\u03b1 \u03bc ?m.63 ?m.64 ?m.65 X"
   ],
   "kind": "type-mismatch",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 419,
   "col": 21,
   "msg": "unsolved goals",
   "detail": [
    "\u03b1 : Type u_1",
    "\u03b2 : Type u_2",
    "\u03a9 : Type u_5",
    "m\u03b1 : MeasurableSpace \u03b1",
    "\u03bc : Measure \u03b1",
    "m\u03b2 : MeasurableSpace \u03b2",
    "inst\u271d\u00b3 : MeasurableSpace \u03a9",
    "inst\u271d\u00b2 : StandardBorelSpace \u03a9",
    "inst\u271d\u00b9 : Nonempty \u03a9",
    "X : \u03b1 \u2192 \u03b2",
    "Y : \u03b1 \u2192 \u03a9",
    "inst\u271d : IsFiniteMeasure \u03bc",
    "f : \u03b2 \u2192 \u03a9",
    "hf : Measurable f",
    "hX : AEMeasurable X \u03bc",
    "hY : AEMeasurable Y \u03bc",
    "h : \u21d1\ud835\udcdb[Y | X; \u03bc] =\u1d50[Measure.map X \u03bc] \u21d1(Kernel.deterministic f hf)",
    "\u22a2 Y =\u1d50[\u03bc] f \u2218 X"
   ],
   "kind": "tactic-failed",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 435,
   "col": 6,
   "msg": "Tactic `rewrite` failed: Did not find an occurrence of the pattern",
   "detail": [
    "  (\ud835\udcdb[?m.86 | Y; ?m.83] ?x) ?s",
    "in the target expression",
    "  (\ud835\udcdb[Y | X; \u03bc] b) s = (Measure.map Y \u03bc[|X \u207b\u00b9' {b}]) s",
    "",
    "\u03b1 : Type u_1",
    "\u03b2 : Type u_2",
    "\u03a9 : Type u_5",
    "m\u03b1 : MeasurableSpace \u03b1",
    "\u03bc : Measure \u03b1",
    "m\u03b2 : MeasurableSpace \u03b2",
    "inst\u271d\u2075 : MeasurableSpace \u03a9",
    "inst\u271d\u2074 : StandardBorelSpace \u03a9",
    "inst\u271d\u00b3 : Nonempty \u03a9",
    "X : \u03b1 \u2192 \u03b2",
    "Y : \u03b1 \u2192 \u03a9",
    "inst\u271d\u00b2 : Countable \u03b2",
    "inst\u271d\u00b9 : MeasurableSingletonClass \u03b2",
    "inst\u271d : IsFiniteMeasure \u03bc",
    "hX : Measurable X",
    "hY : Measurable Y",
    "b : \u03b2",
    "hb : (Measure.map X \u03bc) {b} \u2260 0",
    "s : Set \u03a9",
    "hs : MeasurableSet s",
    "\u22a2 (\ud835\udcdb[Y | X; \u03bc] b) s = (Measure.map Y \u03bc[|X \u207b\u00b9' {b}]) s"
   ],
   "kind": "tactic-failed",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 454,
   "col": 50,
   "msg": "don't know how to synthesize \u0070laceholder for argument `hX`",
   "detail": [
    "context:",
    "\u03b1 : Type u_1",
    "\u03b2 : Type u_2",
    "\u03a9 : Type u_5",
    "\u03a9' : Type u_6",
    "m\u03b1 : MeasurableSpace \u03b1",
    "\u03bc : Measure \u03b1",
    "m\u03b2 : MeasurableSpace \u03b2",
    "inst\u271d\u2076 : MeasurableSpace \u03a9",
    "inst\u271d\u2075 : StandardBorelSpace \u03a9",
    "inst\u271d\u2074 : Nonempty \u03a9",
    "m\u03a9' : MeasurableSpace \u03a9'",
    "inst\u271d\u00b3 : StandardBorelSpace \u03a9'",
    "X : \u03b1 \u2192 \u03b2",
    "Y : \u03b1 \u2192 \u03a9",
    "Z : \u03b1 \u2192 \u03a9'",
    "inst\u271d\u00b2 : Countable \u03a9'",
    "inst\u271d\u00b9 : IsFiniteMeasure \u03bc",
    "hX : Measurable X",
    "hY : Measurable Y",
    "hZ : Measurable Z",
    "\u03ba : Kernel (\u03b2 \u00d7 \u03a9') \u03a9",
    "inst\u271d : IsFiniteKernel \u03ba",
    "h_cond :",
    "  \u2200 (b : \u03a9'), \u03bc (Z \u207b\u00b9' {b}) \u2260 0 \u2192 \u21d1\ud835\udcdb[Y | X; \u03bc[|Z \u207b\u00b9' {b}]] =\u1d50[Measure.map X \u03bc[|Z \u207b\u00b9' {b}]] \u21d1(\u03ba.comap (fun \u03c9 \u21a6 (\u03c9, b)) \u22ef)",
    "\u22a2 AEMeasurable (fun \u03c9 \u21a6 (X \u03c9, Z \u03c9)) \u03bc"
   ],
   "kind": "synth-instance",
   "ident": null
  },
  {
   "sev": "error",
   "file": "LeanMachineLearning/ForMathlib/Probability/Independence/CondDistrib.lean",
   "line": 453,
   "col": 77,
   "msg": "unsolved goals",
   "detail": [
    "\u03b1 : Type u_1",
    "\u03b2 : Type u_2",
    "\u03a9 : Type u_5",
    "\u03a9' : Type u_6",
    "m\u03b1 : MeasurableSpace \u03b1",
    "\u03bc : Measure \u03b1",
    "m\u03b2 : MeasurableSpace \u03b2",
    "inst\u271d\u2076 : MeasurableSpace \u03a9",
    "inst\u271d\u2075 : StandardBorelSpace \u03a9",
    "inst\u271d\u2074 : Nonempty \u03a9",
    "m\u03a9' : MeasurableSpace \u03a9'",
    "inst\u271d\u00b3 : StandardBorelSpace \u03a9'",
    "X : \u03b1 \u2192 \u03b2",
    "Y : \u03b1 \u2192 \u03a9",
    "Z : \u03b1 \u2192 \u03a9'",
    "inst\u271d\u00b2 : Countable \u03a9'",
    "inst\u271d\u00b9 : IsFiniteMeasure \u03bc",
    "hX : Measurable X",
    "hY : Measurable Y",
    "hZ : Measurable Z",
    "\u03ba : Kernel (\u03b2 \u00d7 \u03a9') \u03a9",
    "inst\u271d : IsFiniteKernel \u03ba",
    "h_cond :",
    "  \u2200 (b : \u03a9'), \u03bc (Z \u207b\u00b9' {b}) \u2260 0 \u2192 \u21d1\ud835\udcdb[Y | X; \u03bc[|Z \u207b\u00b9' {b}]] =\u1d50[Measure.map X \u03bc[|Z \u207b\u00b9' {b}]] \u21d1(\u03ba.comap (fun \u03c9 \u21a6 (\u03c9, b)) \u22ef)",
    "\u22a2 \u21d1\ud835\udcdb[Y | fun \u03c9 \u21a6 (X \u03c9, Z \u03c9); \u03bc] =\u1d50[Measure.map (fun \u03c9 \u21a6 (X \u03c9, Z \u03c9)) \u03bc] \u21d1\u03ba"
   ],
   "kind": "tactic-failed",
   "ident": null
  }
 ],
 "deprecations": [
  {
   "file": "LeanMachineLearning/ForMathlib/Probability/Kernel/KernelSub.lean",
   "line": 77,
   "old": "dif_pos",
   "new": null
  },
  {
   "file": "LeanMachineLearning/ForMathlib/Probability/Kernel/KernelSub.lean",
   "line": 152,
   "old": "Set.mem_setOf_eq",
   "new": null
  },
  {
   "file": "LeanMachineLearning/ForMathlib/MeasureTheory/Order/MeasurableArg.lean",
   "line": 77,
   "old": "Set.mem_setOf_eq",
   "new": null
  }
 ]
}