MATLAB Examples

meanMCBer_g

Monte Carlo method to estimate the mean of a Bernoulli random variable to within a specified absolute error tolerance with guaranteed confidence level 1-alpha.

Contents

Syntax

pHat = meanMCBer_g(Yrand)

pHat = meanMCBer_g(Yrand,abstol,alpha,nmax)

pHat = meanMCBer_g(Yrand,'abstol',abstol,'alpha',alpha,'nmax',nmax)

[pHat, out_param] = meanMCBer_g(Yrand,in_param)

Description

pHat = meanMCBer_g(Yrand) estimates the mean of a Bernoulli random variable Y to within a specified absolute error tolerance with guaranteed confidence level 99%. Input Yrand is a function handle that accepts a positive integer input n and returns a n x 1 vector of IID instances of the Bernoulli random variable Y.

pHat = meanMCBer_g(Yrand,abstol,alpha,nmax) estimates the mean of a Bernoulli random variable Y to within a specified absolute error tolerance with guaranteed confidence level 1-alpha using all ordered parsing inputs abstol, alpha and nmax.

pHat = meanMCBer_g(Yrand,'abstol',abstol,'alpha',alpha,'nmax',nmax) estimates the mean of a Bernoulli random variable Y to within a specified absolute error tolerance with guaranteed confidence level 1-alpha. All the field-value pairs are optional and can be supplied in different order.

[pHat, out_param] = meanMCBer_g(Yrand,in_param) estimates the mean of a Bernoulli random variable Y to within a specified absolute error tolerance with the given parameters in_param and produce the estimated mean pHat and output parameters out_param.

Input Arguments

  • Yrand --- the function for generating IID instances of a Bernoulli random variable Y whose mean we want to estimate.
  • pHat --- the estimated mean of Y.
  • in_param.abstol --- the absolute error tolerance, the default value is 1e-2.
  • in_param.alpha --- the uncertainty, the default value is 1%.
  • in_param.nmax --- the sample budget, the default value is 1e9.

Output Arguments

  • out_param.n --- the total sample used.
  • out_param.time --- the time elapsed in seconds.

  • out_param.exit --- the state of program when exiting:
    • 0 success
    • 1 Not enough samples to estimate p with guarantee

Guarantee

If the sample size is calculated according Hoeffding's inequality, which equals to ceil(log(2/out_param.alpha)/(2*out_param.abstol^2)), then the following inequality must be satisfied:

Pr(| p - pHat | <= abstol) >= 1-alpha.

Here p is the true mean of Yrand, and pHat is the output of MEANMCBER_G.

Also, the cost is deterministic.

Examples

Example 1

% Calculate the mean of a Bernoulli random variable with true p=1/90,
% absolute error tolerance 1e-3 and uncertainty 0.01.

    in_param.abstol=1e-3; in_param.alpha = 0.01; in_param.nmax = 1e9;
    p=1/9; Yrand=@(n) rand(n,1)<p;
    pHat = meanMCBer_g(Yrand,in_param)
pHat =

    0.1110

Example 2

% Using the same function as example 1, with the absolute error tolerance
% 1e-4.

    pHat = meanMCBer_g(Yrand,1e-4)
pHat =

    0.1111

Example 3

% Using the same function as example 1, with the absolute error tolerance
% 1e-2 and uncertainty 0.05.

    pHat = meanMCBer_g(Yrand,'abstol',1e-2,'alpha',0.05)
pHat =

    0.1115

See Also

funappx_g

integral_g

cubMC_g

meanMC_g

cubLattice_g

cubSobol_g

References

[1] F. J. Hickernell, L. Jiang, Y. Liu, and A. B. Owen, Guaranteed conservative fixed width confidence intervals via Monte Carlo sampling, Monte Carlo and Quasi-Monte Carlo Methods 2012 (J. Dick, F. Y. Kuo, G. W. Peters, and I. H. Sloan, eds.), Springer-Verlag, Berlin, 2014. arXiv:1208.4318 [math.ST]

[2] Lan Jiang and Fred J. Hickernell, Guaranteed Conservative Confidence Intervals for Means of Bernoulli Random Variables, submitted for publication, 2014.

[3] Sou-Cheng T. Choi, Yuhan Ding, Fred J. Hickernell, Lan Jiang, Lluis Antoni Jimenez Rugama, Xin Tong, Yizhi Zhang and Xuan Zhou, GAIL: Guaranteed Automatic Integration Library (Version 2.1) [MATLAB Software], 2015. Available from http://code.google.com/p/gail/

[4] Sou-Cheng T. Choi, MINRES-QLP Pack and Reliable Reproducible Research via Supportable Scientific Software, Journal of Open Research Software, Volume 2, Number 1, e22, pp. 1-7, 2014.

[5] Sou-Cheng T. Choi and Fred J. Hickernell, IIT MATH-573 Reliable Mathematical Software [Course Slides], Illinois Institute of Technology, Chicago, IL, 2013. Available from http://code.google.com/p/gail/

[6] Daniel S. Katz, Sou-Cheng T. Choi, Hilmar Lapp, Ketan Maheshwari, Frank Loffler, Matthew Turk, Marcus D. Hanwell, Nancy Wilkins-Diehr, James Hetherington, James Howison, Shel Swenson, Gabrielle D. Allen, Anne C. Elster, Bruce Berriman, Colin Venters, Summary of the First Workshop On Sustainable Software for Science: Practice And Experiences (WSSSPE1), Journal of Open Research Software, Volume 2, Number 1, e6, pp. 1-21, 2014.

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