mme_beta

  Signature: (a(n); float+ [o]alpha(); float+ [o]beta())

    my ($a, $b) = $data->mme_beta();

beta distribution. pdf: f(x; a,b) = 1/B(a,b) x^(a-1) (1-x)^(b-1)

mme_beta processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pdf_beta

  Signature: (x(); a(); b(); float+ [o]p())

probability density function for beta distribution. x defined on [0,1].

pdf_beta processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

mme_binomial

  Signature: (a(n); int [o]n_(); float+ [o]p())

    my ($n, $p) = $data->mme_binomial;

binomial distribution. pmf: f(k; n,p) = (n k) p^k (1-p)^(n-k) for k = 0,1,2..n

mme_binomial processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pmf_binomial

  Signature: (ushort x(); ushort n(); p(); float+ [o]out())

probability mass function for binomial distribution.

pmf_binomial processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

mle_exp

  Signature: (a(n); float+ [o]l())

    my $lamda = $data->mle_exp;

exponential distribution. mle same as method of moments estimate.

mle_exp processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pdf_exp

  Signature: (x(); l(); float+ [o]p())

probability density function for exponential distribution.

pdf_exp processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

mme_gamma

  Signature: (a(n); float+ [o]shape(); float+ [o]scale())

    my ($shape, $scale) = $data->mme_gamma();

two-parameter gamma distribution

mme_gamma processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pdf_gamma

  Signature: (x(); a(); t(); float+ [o]p())

probability density function for two-parameter gamma distribution.

pdf_gamma processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

mle_gaussian

  Signature: (a(n); float+ [o]m(); float+ [o]v())

    my ($m, $v) = $data->mle_gaussian();

gaussian aka normal distribution. same results as $data->average and $data->var. mle same as method of moments estimate.

mle_gaussian processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pdf_gaussian

  Signature: (x(); m(); v(); float+ [o]p())

probability density function for gaussian distribution.

pdf_gaussian processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

mle_geo

  Signature: (a(n); float+ [o]p())

geometric distribution. mle same as method of moments estimate.

mle_geo processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pmf_geo

  Signature: (ushort x(); p(); float+ [o]out())

probability mass function for geometric distribution. x >= 0.

pmf_geo processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

mle_geosh

  Signature: (a(n); float+ [o]p())

shifted geometric distribution. mle same as method of moments estimate.

mle_geosh processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pmf_geosh

  Signature: (ushort x(); p(); float+ [o]out())

probability mass function for shifted geometric distribution. x >= 1.

pmf_geosh processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

mle_lognormal

  Signature: (a(n); float+ [o]m(); float+ [o]v())

    my ($m, $v) = $data->mle_lognormal();

lognormal distribution. maximum likelihood estimation.

mle_lognormal processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

mme_lognormal

  Signature: (a(n); float+ [o]m(); float+ [o]v())

    my ($m, $v) = $data->mme_lognormal();

lognormal distribution. method of moments estimation.

mme_lognormal processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pdf_lognormal

  Signature: (x(); m(); v(); float+ [o]p())

probability density function for lognormal distribution. x > 0. v > 0.

pdf_lognormal processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

mme_nbd

  Signature: (a(n); float+ [o]r(); float+ [o]p())

    my ($r, $p) = $data->mme_nbd();

negative binomial distribution. pmf: f(x; r,p) = (x+r-1 r-1) p^r (1-p)^x for x=0,1,2...

mme_nbd processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pmf_nbd

  Signature: (ushort x(); r(); p(); float+ [o]out())

probability mass function for negative binomial distribution.

pmf_nbd processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

mme_pareto

  Signature: (a(n); float+ [o]k(); float+ [o]xm())

    my ($k, $xm) = $data->mme_pareto();

pareto distribution. pdf: f(x; k,xm) = k xm^k / x^(k+1) for x >= xm > 0.

mme_pareto processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pdf_pareto

  Signature: (x(); k(); xm(); float+ [o]p())

probability density function for pareto distribution. x >= xm > 0.

pdf_pareto processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

mle_poisson

  Signature: (a(n); float+ [o]l())

    my $lamda = $data->mle_poisson();

poisson distribution. pmf: f(x;l) = e^(-l) * l^x / x!

mle_poisson processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pmf_poisson

  Signature: (x(); l(); float+ [o]p())

Probability mass function for poisson distribution. Uses Stirling's formula for x > 85.

pmf_poisson processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pmf_poisson_stirling

  Signature: (x(); l(); [o]p())

Probability mass function for poisson distribution. Uses Stirling's formula for all values of the input. See http://en.wikipedia.org/wiki/Stirling's_approximation for more info.

pmf_poisson_stirling processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pmf_poisson_factorial

  Signature: ushort x(); l(); float+ [o]p()

Probability mass function for poisson distribution. Input is limited to x < 170 to avoid gsl_sf_fact() overflow.

plot_distr

Plots data distribution. When given specific distribution(s) to fit, returns % ref to sum log likelihood and parameter values under fitted distribution(s). See FUNCTIONS above for available distributions.

Default options (case insensitive):

    MAXBN => 20, 
      # see PDL::Graphics::PGPLOT::Window for next options
    WIN   => undef,   # pgwin object. not closed here if passed
                      # allows comparing multiple distr in same plot
                      # set env before passing WIN
    DEV   => '/xs' ,  # open and close dev for plotting if no WIN
                      # defaults to '/png' in Windows
    COLOR => 1,       # color for data distr

Usage:

      # yes it threads :)
    my $data = grandom( 500, 3 )->abs;
      # ll on plot is sum across 3 data curves
    my ($ll, $pars)
      = $data->plot_distr( 'gaussian', 'lognormal', {DEV=>'/png'} );
      # pars are from normalized data (ie data / bin_size)
    print "$_\t@{$pars->{$_}}\n" for (sort keys %$pars);
    print "$_\t$ll->{$_}\n" for (sort keys %$ll);