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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 acf(x: Seq[Double], lm: Int): DenseVector[Double]

   Autocorrelation function

   Autocorrelation function

   x

   Input data

   lm

   Maximum lag required

   returns

   A vector of autocorrelations

5.  final def asInstanceOf[T0]: T0

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6.  def clone(): AnyRef

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

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8.  def equals(arg0: Any): Boolean

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9.  def finalize(): Unit

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10.  final def getClass(): Class[_]

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11.  def hashCode(): Int

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12.  final def isInstanceOf[T0]: Boolean

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13.  def mhStream[P](init: P, logLik: (P) ⇒ LogLik, rprop: (P) ⇒ P, dprop: (P, P) ⇒ LogLik, dprior: (P) ⇒ LogLik, verb: Boolean = false): Stream[P]

    Function to construct a generic Metropolis-Hastings MCMC algorithm for Bayesian inference.

    Function to construct a generic Metropolis-Hastings MCMC algorithm for Bayesian inference. Note that this algorithm avoids re-computation of the log-likelihood associated with the current state, and is therefore suitable for use with the log of an unbiased estimate of likelihood for the constuction of pseudo-marginal "exact approximate" MCMC algorithms.

    init

    The initial state of the MCMC algorithm

    logLik

    The log-likelihood of the model

    rprop

    A function to sample from a proposal distribution

    dprop

    A function to evaluate the log-likelihood of the proposal transition kernel

    dprior

    A function to evaluate the log of the prior density

    verb

    Should the function print diagnostic information to the console at each iteration?

    returns

    An infinite Stream corresponding to the MCMC chain. Note that this can be processed with typical Scala combinators such as drop (for burn-in) and take (for run-length). If Types are imported, there is also a thin method (which can be used for thinning the chain).

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

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15.  def nextValue[P](logLik: (P) ⇒ LogLik, rprop: (P) ⇒ P, dprop: (P, P) ⇒ LogLik, dprior: (P) ⇒ LogLik, verb: Boolean = false)(current: (P, LogLik)): (P, LogLik)

    Function for executing one step of a MH algorithm.

    Function for executing one step of a MH algorithm. Called by mhStream.

16.  final def notify(): Unit

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17.  final def notifyAll(): Unit

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18.  def summary[P](s: GenSeq[P], plot: Boolean)(implicit arg0: CsvRow[P]): Unit

    Wrapper around the other summary function which takes a Stream

    Wrapper around the other summary function which takes a Stream

    s

    A *finite* stream of MCMC iterations.

    plot

    Generate plots?

19.  def summary(m: DenseMatrix[Double], plt: Boolean = true, lm: Int = 50): Unit

    Generate some basic diagnostics associated with an MCMC run.

    Generate some basic diagnostics associated with an MCMC run. Called purely for the side-effect of generating output on the console.

    m

    A matrix, such as generated by toDMD containing MCMC output

    plt

    Generate plots?

    lm

    Max lag for ACF plot

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

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21.  def toDMD[P](s: GenSeq[P])(implicit arg0: CsvRow[P]): DenseMatrix[Double]

    Utility function to convert a *finite* Stream (or other collection) to a Breeze DenseMatrix[Double].

    Utility function to convert a *finite* Stream (or other collection) to a Breeze DenseMatrix[Double].

    s

    Input stream/collection, which must be *finite*.

    returns

    A matrix with rows corresponding to iterations and columns corresponding to variables.

22.  def toString(): String

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23.  final def wait(): Unit

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

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

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