Examples

Value Members

1. final def !=(arg0: Any): Boolean

   

   Definition Classes

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. object A extends BloodType with Product with Serializable

   

5. object AB extends BloodType with Product with Serializable

   

6. object A_ extends BloodGene with Product with Serializable

   

7. object B extends BloodType with Product with Serializable

   

8. object B_ extends BloodGene with Product with Serializable

   

9. implicit object BloodTypeOrd extends Ordering[BloodType]

   

10. object Boy extends Child with Product with Serializable

    

11. object Democrat extends Party with Product with Serializable

    

12. object Girl extends Child with Product with Serializable

    

13. object North extends State with Product with Serializable

    

14. object O extends BloodType with Product with Serializable

    

15. object O_ extends BloodGene with Product with Serializable

    

16. def Q(y: Boolean): Distribution[Boolean]

    

17. object Republican extends Party with Product with Serializable

    

18. object Sick extends Patient with Product with Serializable

    

19. object Smoking

    

20. implicit val SmokingTrialOrdering: Ordering[SmokingTrial]

    

21. object South extends State with Product with Serializable

    

22. object Test

    

23. def W(z: Boolean): Distribution[Boolean]

    

24. object Well extends Patient with Product with Serializable

    

25. def X: Distribution[Boolean]

    

    The probablistic graphical model

    The probablistic graphical model

    Y -> Q | v X -> Z -> W

26. def Y: Distribution[Boolean]

    

27. def Z(x: Boolean, y: Boolean): Distribution[Boolean]

    

28. final def asInstanceOf[T0]: T0

    

    Definition Classes

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29. def attack(a: Int, d: Int): Distribution[Int]

    

    RISK

30. def attacks(a: Int, d: Int): Distribution[(Int, Int)]

    

31. def bayesianCoin(nflips: Int): Distribution[CoinTrial]

    

32. def bayesianCoin2(nflips: Int): Distribution[Boolean]

    

33. val bloodPrior: Distribution[(BloodGene, BloodGene)]

    

34. val bloodType: Distribution[BloodTrial]

    

35. def cancer(smoker: Boolean, tar: Boolean): Distribution[Boolean]

    

    Observed probabilities of getting cancer broken out by whether you smoke and whether you have tar in your lungs

36. def centralLimitTheorem1(d: Distribution[Double], samples: Int): Unit

    

37. def centralLimitTheorem2(d: Distribution[Boolean], samples: Int): Unit

    

38. def centralLimitTheorem3(d1: Distribution[Double], d2: Distribution[Double], samples1: Int, samples2: Int): Unit

    

39. def childFromParents(p1: (BloodGene, BloodGene), p2: (BloodGene, BloodGene)): Distribution[(BloodGene, BloodGene)]

    

40. def clone(): AnyRef

    

    Attributes

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    @throws( ... )

41. def conquest(a: Int, ds: List[Int]): Distribution[Int]

    

42. def dep[A, B](p: Distribution[A])(e1: (A) ⇒ B, e2: (A) ⇒ B)(implicit ord: Ordering[B]): Unit

    

43. def dieSum(rolls: Int): Distribution[List[Int]]

    

    You roll a 6-sided die and keep a running sum.

    You roll a 6-sided die and keep a running sum. What is the probability the sum reaches exactly 30?

44. def differenceOfMeans(d1: Distribution[Double], d2: Distribution[Double], n1: Int, n2: Int): Distribution[Double]

    

45. def doPGM: Unit

    

46. def doSmoking: Distribution[SmokingTrial]

    

47. def elisa: Distribution[(Patient, Boolean)]

    

48. final def eq(arg0: AnyRef): Boolean

    

    Definition Classes

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49. def equals(arg0: Any): Boolean

    

    Definition Classes

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50. def expectedFlips(flips: Int): Distribution[Int]

    

    How many times do you need to flip a fair coin to get n heads in a row?

51. def family: Distribution[List[Product with Serializable with Child]]

    

52. def finalize(): Unit

    

    Attributes

    protected[java.lang]

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    @throws( classOf[java.lang.Throwable] )

53. final def getClass(): Class[_]

    

    Definition Classes

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54. def hashCode(): Int

    

    Definition Classes

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55. def hth: Distribution[Int]

    

    If you flip a coin repeatedly, which is more likely to occur first, HTH or HTT?

56. def htt: Distribution[Int]

    

57. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

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58. val jones: Distribution[List[Product with Serializable with Child]]

    

    Mr.

    Mr. Jones has two children. The older child is a girl. What is the probability that both children are girls?

59. val montyHall: Distribution[(Int, Int)]

    

    Monty Hall problem

60. final def ne(arg0: AnyRef): Boolean

    

    Definition Classes

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61. final def notify(): Unit

    

    Definition Classes

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62. final def notifyAll(): Unit

    

    Definition Classes

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63. def pascal(depth: Int): Distribution[(Int, Int)]

    

    Pascal's triangle

64. def pgm: Distribution[Trial]

    

65. def population(families: Int): Distribution[Double]

    

66. def queue(loadFactor: Double): Distribution[Int]

    

    A single bank teller can service a customer in 10 minutes.

    A single bank teller can service a customer in 10 minutes. If one customer comes in every 11 minutes on average, what is the expected length of the line?

67. def randomWalk(target: Int, maxSteps: Int): Distribution[List[Int]]

    

    Random walk: starting at 0 and moving left or right with equal probability, how many steps do you expect to take before reaching 10?

68. def runBank: Double

    

69. def runBayesianCoin(heads: Int): Double

    

70. def runBayesianCoin2: Unit

    

71. def runBloodType: Double

    

72. def runBoyGirl: Double

    

73. def runCentralLimitTheorem1: Unit

    

74. def runCentralLimitTheorem2: Unit

    

75. def runCentralLimitTheorem3: Unit

    

76. def runConquest: Unit

    

77. def runDieSum: Double

    

78. def runElisa: Double

    

79. def runJones: Double

    

80. def runKSTest: Unit

    

81. def runMontyHall: Double

    

82. def runPascal: Unit

    

83. def runRandomWalk: Double

    

84. def runSimpsonDem(): Double

    

85. def runSimpsonRep(): Double

    

86. def runSmith: Double

    

87. def runSmoking: Unit

    

88. def runTuesday: Double

    

89. def runUnknownBiasedCoin: Unit

    

90. def runUnknownBiasedCoin2: Unit

    

91. def runWeather: Distribution[Boolean]

    

92. def simpson(): Distribution[(Party, State, Boolean)]

    

93. val smith: Distribution[List[Product with Serializable with Child]]

    

    Mr.

    Mr. Smith has two children. At least one of them is a boy. What is the probability that both children are boys?

94. def smoker: Distribution[Boolean]

    

    Teasing apart correlation and causality.

    Teasing apart correlation and causality. From http://www.michaelnielsen.org/ddi/if-correlation-doesnt-imply-causation-then-what-does/

    First, observe the joint probability distribution of:

    • whether someone smokes
    • whether someone has tar in their lungs
    • whether someone gets cancer

    Encode this as a graphical model.

95. def smoking: Distribution[SmokingTrial]

    

    This encodes the probability distribution of smoking and cancer.

    This encodes the probability distribution of smoking and cancer. We can use it to calculate p(cancer) and p(cancer|smoking).

96. final def synchronized[T0](arg0: ⇒ T0): T0

    

    Definition Classes

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97. def tar(smoker: Boolean): Distribution[Boolean]

    

    95% of smokers have tar in their lungs 5% of nonsmokers have tar in their lungs

98. def testBetaApproximatesNormal(a: Int, b: Int): Double

    

99. def toString(): String

    

    Definition Classes

    AnyRef → Any

100. val tuesday: Distribution[List[(Product with Serializable with Child, Int)]]

     

     Mr.

     Mr. Miller has two children. One of them is a boy born on Tuesday. What is the probability both children are boys?

101. def typeFromGene(g: (BloodGene, BloodGene)): Distribution[BloodType]

     

102. def unknownBiasedCoin(prior: Distribution[Double], nflips: Int, successes: Int): Distribution[Double]

     

     Given a biased coin with unknown bias distributed according to prior, after flipping nflips times and observing successes heads, what is the posterior distribution of the bias?

103. def unknownBiasedCoin2(prior: Distribution[Double], nflips: Int, successes: Int): Distribution[Double]

     

104. final def wait(): Unit

     

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105. final def wait(arg0: Long, arg1: Int): Unit

     

     Definition Classes

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106. final def wait(arg0: Long): Unit

     

     Definition Classes

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107. def weather: Distribution[(Boolean, Boolean)]

     

     Given a Markov model for the weather, if it is rainy on thursday, what was the likely weather on monday?