Value Members

1.  final def !=(arg0: Any): Boolean

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2.  final def ##(): Int

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3.  final def ==(arg0: Any): Boolean

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4.  def aOrNotB(a: LogProbability, b: LogProbability): LogProbability

   Computes the probability Pr(A OR not B) = Pr(A) - Pr(B).

   Computes the probability Pr(A OR not B) = Pr(A) - Pr(B). While this could be computed using or(a, not(b)), this form is more efficient and more precise.

5.  def and(values: Array[Double]): LogProbability

   Computes the probability of the given independent events co-occurring: Pr(AB..N) = Pr(A)*Pr(B)*...*Pr(N).

6.  def and(a: LogProbability, b: LogProbability): LogProbability

   Computes the probability of a and b, where a and b are independent events: Pr(AB) = Pr(A)*Pr(B).

7.  final def asInstanceOf[T0]: T0

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8.  def clone(): AnyRef

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   protected[java.lang]

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   @throws( ... )

9.  final def eq(arg0: AnyRef): Boolean

   Definition Classes

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10.  def equals(arg0: Any): Boolean

    Definition Classes

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11.  def expProb(p: LogProbability): Double

    Exponentiates a LogProbability and limits the return to the range 0-1 to gracefully handle probabilities with minor numerical imprecisions.

    Exponentiates a LogProbability and limits the return to the range 0-1 to gracefully handle probabilities with minor numerical imprecisions. If the LogProbability does not represent a probability between 0 and 1 Math.exp should be used instead.

12.  def finalize(): Unit

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    protected[java.lang]

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

13.  def fromPhredScore(s: Int): LogProbability

    Takes a phred score and converts it into the natural log of the probability of error.

14.  def fromPhredScore(s: PhredScore): LogProbability

    Takes a phred score and converts it into the natural log of the probability of error.

15.  final def getClass(): Class[_]

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16.  def hashCode(): Int

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17.  final def isInstanceOf[T0]: Boolean

    Definition Classes

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18.  final def ne(arg0: AnyRef): Boolean

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19.  def normalizeByLogProbability(p: LogProbability, divideBy: LogProbability): LogProbability

    Normalizes a probability.

20.  def normalizeByScalar(p: LogProbability, divideBy: Int): LogProbability

    Normalizes a probability.

21.  def not(a: LogProbability): LogProbability

    Returns the probability of Pr(not A) = 1 - Pr(A).

22.  final def notify(): Unit

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23.  final def notifyAll(): Unit

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24.  def or(values: Array[LogProbability]): LogProbability

    Computes the probability of any of the given independent events occurring: Pr(AB..N) = Pr(A)+Pr(B)+...+Pr(N).

25.  def or(a: LogProbability, b: LogProbability): LogProbability

    Computes the probability of a or b, where a and b are independent events: Pr(A or B) = Pr(A) + Pr(B).

26.  def probabilityOfErrorTwoTrials(prErrorTrialOne: LogProbability, prErrorTrialTwo: LogProbability): LogProbability

    Computes the probability of seeing an error in the base sequence if there are two independent error processes.

    Computes the probability of seeing an error in the base sequence if there are two independent error processes. We sum three terms: 1. the probability of an error in trial one and no error in trial two: Pr(A=Error, B=NoError). 2. the probability of no error in trial one and an error in trial two: Pr(A=NoError, B=Error). 3. the probability of an error in both trials, but when the second trial does not reverse the error in first one, which for DNA (4 bases) would only occur 2/3 times: Pr(A=x->y, B=y->z) * Pr(x!=z | x!=y, y!=z, x,y,z \in {A,C,G,T})

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

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28.  def toLogProbability(value: Double): LogProbability

    Converts the double to log-space.

29.  def toString(): String

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30.  final def wait(): Unit

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    @throws( ... )

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

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    @throws( ... )

32.  final def wait(arg0: Long): Unit

    Definition Classes

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    @throws( ... )