236 lines
11 KiB
Python
236 lines
11 KiB
Python
r"""
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====================================================
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Quasi-Monte Carlo submodule (:mod:`scipy.stats.qmc`)
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====================================================
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.. currentmodule:: scipy.stats.qmc
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This module provides Quasi-Monte Carlo generators and associated helper
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functions.
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Quasi-Monte Carlo
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=================
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Engines
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-------
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.. autosummary::
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:toctree: generated/
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QMCEngine
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Sobol
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Halton
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LatinHypercube
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PoissonDisk
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MultinomialQMC
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MultivariateNormalQMC
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Helpers
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-------
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.. autosummary::
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:toctree: generated/
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discrepancy
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geometric_discrepancy
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update_discrepancy
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scale
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Introduction to Quasi-Monte Carlo
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=================================
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Quasi-Monte Carlo (QMC) methods [1]_, [2]_, [3]_ provide an
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:math:`n \times d` array of numbers in :math:`[0,1]`. They can be used in
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place of :math:`n` points from the :math:`U[0,1]^{d}` distribution. Compared to
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random points, QMC points are designed to have fewer gaps and clumps. This is
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quantified by discrepancy measures [4]_. From the Koksma-Hlawka
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inequality [5]_ we know that low discrepancy reduces a bound on
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integration error. Averaging a function :math:`f` over :math:`n` QMC points
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can achieve an integration error close to :math:`O(n^{-1})` for well
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behaved functions [2]_.
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Most QMC constructions are designed for special values of :math:`n`
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such as powers of 2 or large primes. Changing the sample
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size by even one can degrade their performance, even their
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rate of convergence [6]_. For instance :math:`n=100` points may give less
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accuracy than :math:`n=64` if the method was designed for :math:`n=2^m`.
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Some QMC constructions are extensible in :math:`n`: we can find
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another special sample size :math:`n' > n` and often an infinite
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sequence of increasing special sample sizes. Some QMC
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constructions are extensible in :math:`d`: we can increase the dimension,
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possibly to some upper bound, and typically without requiring
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special values of :math:`d`. Some QMC methods are extensible in
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both :math:`n` and :math:`d`.
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QMC points are deterministic. That makes it hard to estimate the accuracy of
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integrals estimated by averages over QMC points. Randomized QMC (RQMC) [7]_
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points are constructed so that each point is individually :math:`U[0,1]^{d}`
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while collectively the :math:`n` points retain their low discrepancy.
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One can make :math:`R` independent replications of RQMC points to
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see how stable a computation is. From :math:`R` independent values,
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a t-test (or bootstrap t-test [8]_) then gives approximate confidence
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intervals on the mean value. Some RQMC methods produce a
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root mean squared error that is actually :math:`o(1/n)` and smaller than
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the rate seen in unrandomized QMC. An intuitive explanation is
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that the error is a sum of many small ones and random errors
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cancel in a way that deterministic ones do not. RQMC also
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has advantages on integrands that are singular or, for other
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reasons, fail to be Riemann integrable.
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(R)QMC cannot beat Bahkvalov's curse of dimension (see [9]_). For
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any random or deterministic method, there are worst case functions
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that will give it poor performance in high dimensions. A worst
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case function for QMC might be 0 at all n points but very
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large elsewhere. Worst case analyses get very pessimistic
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in high dimensions. (R)QMC can bring a great improvement over
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MC when the functions on which it is used are not worst case.
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For instance (R)QMC can be especially effective on integrands
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that are well approximated by sums of functions of
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some small number of their input variables at a time [10]_, [11]_.
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That property is often a surprising finding about those functions.
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Also, to see an improvement over IID MC, (R)QMC requires a bit of smoothness of
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the integrand, roughly the mixed first order derivative in each direction,
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:math:`\partial^d f/\partial x_1 \cdots \partial x_d`, must be integral.
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For instance, a function that is 1 inside the hypersphere and 0 outside of it
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has infinite variation in the sense of Hardy and Krause for any dimension
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:math:`d = 2`.
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Scrambled nets are a kind of RQMC that have some valuable robustness
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properties [12]_. If the integrand is square integrable, they give variance
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:math:`var_{SNET} = o(1/n)`. There is a finite upper bound on
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:math:`var_{SNET} / var_{MC}` that holds simultaneously for every square
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integrable integrand. Scrambled nets satisfy a strong law of large numbers
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for :math:`f` in :math:`L^p` when :math:`p>1`. In some
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special cases there is a central limit theorem [13]_. For smooth enough
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integrands they can achieve RMSE nearly :math:`O(n^{-3})`. See [12]_
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for references about these properties.
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The main kinds of QMC methods are lattice rules [14]_ and digital
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nets and sequences [2]_, [15]_. The theories meet up in polynomial
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lattice rules [16]_ which can produce digital nets. Lattice rules
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require some form of search for good constructions. For digital
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nets there are widely used default constructions.
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The most widely used QMC methods are Sobol' sequences [17]_.
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These are digital nets. They are extensible in both :math:`n` and :math:`d`.
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They can be scrambled. The special sample sizes are powers
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of 2. Another popular method are Halton sequences [18]_.
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The constructions resemble those of digital nets. The earlier
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dimensions have much better equidistribution properties than
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later ones. There are essentially no special sample sizes.
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They are not thought to be as accurate as Sobol' sequences.
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They can be scrambled. The nets of Faure [19]_ are also widely
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used. All dimensions are equally good, but the special sample
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sizes grow rapidly with dimension :math:`d`. They can be scrambled.
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The nets of Niederreiter and Xing [20]_ have the best asymptotic
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properties but have not shown good empirical performance [21]_.
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Higher order digital nets are formed by a digit interleaving process
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in the digits of the constructed points. They can achieve higher
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levels of asymptotic accuracy given higher smoothness conditions on :math:`f`
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and they can be scrambled [22]_. There is little or no empirical work
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showing the improved rate to be attained.
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Using QMC is like using the entire period of a small random
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number generator. The constructions are similar and so
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therefore are the computational costs [23]_.
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(R)QMC is sometimes improved by passing the points through
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a baker's transformation (tent function) prior to using them.
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That function has the form :math:`1-2|x-1/2|`. As :math:`x` goes from 0 to
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1, this function goes from 0 to 1 and then back. It is very
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useful to produce a periodic function for lattice rules [14]_,
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and sometimes it improves the convergence rate [24]_.
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It is not straightforward to apply QMC methods to Markov
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chain Monte Carlo (MCMC). We can think of MCMC as using
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:math:`n=1` point in :math:`[0,1]^{d}` for very large :math:`d`, with
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ergodic results corresponding to :math:`d \to \infty`. One proposal is
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in [25]_ and under strong conditions an improved rate of convergence
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has been shown [26]_.
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Returning to Sobol' points: there are many versions depending
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on what are called direction numbers. Those are the result of
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searches and are tabulated. A very widely used set of direction
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numbers come from [27]_. It is extensible in dimension up to
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:math:`d=21201`.
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References
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----------
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.. [1] Owen, Art B. "Monte Carlo Book: the Quasi-Monte Carlo parts." 2019.
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.. [2] Niederreiter, Harald. "Random number generation and quasi-Monte Carlo
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methods." Society for Industrial and Applied Mathematics, 1992.
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.. [3] Dick, Josef, Frances Y. Kuo, and Ian H. Sloan. "High-dimensional
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integration: the quasi-Monte Carlo way." Acta Numerica no. 22: 133, 2013.
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.. [4] Aho, A. V., C. Aistleitner, T. Anderson, K. Appel, V. Arnol'd, N.
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Aronszajn, D. Asotsky et al. "W. Chen et al.(eds.), "A Panorama of
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Discrepancy Theory", Sringer International Publishing,
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Switzerland: 679, 2014.
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.. [5] Hickernell, Fred J. "Koksma-Hlawka Inequality." Wiley StatsRef:
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Statistics Reference Online, 2014.
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.. [6] Owen, Art B. "On dropping the first Sobol' point." :arxiv:`2008.08051`,
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2020.
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.. [7] L'Ecuyer, Pierre, and Christiane Lemieux. "Recent advances in randomized
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quasi-Monte Carlo methods." In Modeling uncertainty, pp. 419-474. Springer,
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New York, NY, 2002.
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.. [8] DiCiccio, Thomas J., and Bradley Efron. "Bootstrap confidence
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intervals." Statistical science: 189-212, 1996.
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.. [9] Dimov, Ivan T. "Monte Carlo methods for applied scientists." World
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Scientific, 2008.
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.. [10] Caflisch, Russel E., William J. Morokoff, and Art B. Owen. "Valuation
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of mortgage backed securities using Brownian bridges to reduce effective
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dimension." Journal of Computational Finance: no. 1 27-46, 1997.
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.. [11] Sloan, Ian H., and Henryk Wozniakowski. "When are quasi-Monte Carlo
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algorithms efficient for high dimensional integrals?." Journal of Complexity
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14, no. 1 (1998): 1-33.
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.. [12] Owen, Art B., and Daniel Rudolf, "A strong law of large numbers for
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scrambled net integration." SIAM Review, to appear.
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.. [13] Loh, Wei-Liem. "On the asymptotic distribution of scrambled net
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quadrature." The Annals of Statistics 31, no. 4: 1282-1324, 2003.
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.. [14] Sloan, Ian H. and S. Joe. "Lattice methods for multiple integration."
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Oxford University Press, 1994.
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.. [15] Dick, Josef, and Friedrich Pillichshammer. "Digital nets and sequences:
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discrepancy theory and quasi-Monte Carlo integration." Cambridge University
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Press, 2010.
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.. [16] Dick, Josef, F. Kuo, Friedrich Pillichshammer, and I. Sloan.
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"Construction algorithms for polynomial lattice rules for multivariate
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integration." Mathematics of computation 74, no. 252: 1895-1921, 2005.
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.. [17] Sobol', Il'ya Meerovich. "On the distribution of points in a cube and
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the approximate evaluation of integrals." Zhurnal Vychislitel'noi Matematiki
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i Matematicheskoi Fiziki 7, no. 4: 784-802, 1967.
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.. [18] Halton, John H. "On the efficiency of certain quasi-random sequences of
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points in evaluating multi-dimensional integrals." Numerische Mathematik 2,
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no. 1: 84-90, 1960.
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.. [19] Faure, Henri. "Discrepance de suites associees a un systeme de
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numeration (en dimension s)." Acta arithmetica 41, no. 4: 337-351, 1982.
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.. [20] Niederreiter, Harold, and Chaoping Xing. "Low-discrepancy sequences and
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global function fields with many rational places." Finite Fields and their
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applications 2, no. 3: 241-273, 1996.
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.. [21] Hong, Hee Sun, and Fred J. Hickernell. "Algorithm 823: Implementing
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scrambled digital sequences." ACM Transactions on Mathematical Software
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(TOMS) 29, no. 2: 95-109, 2003.
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.. [22] Dick, Josef. "Higher order scrambled digital nets achieve the optimal
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rate of the root mean square error for smooth integrands." The Annals of
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Statistics 39, no. 3: 1372-1398, 2011.
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.. [23] Niederreiter, Harald. "Multidimensional numerical integration using
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pseudorandom numbers." In Stochastic Programming 84 Part I, pp. 17-38.
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Springer, Berlin, Heidelberg, 1986.
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.. [24] Hickernell, Fred J. "Obtaining O (N-2+e) Convergence for Lattice
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Quadrature Rules." In Monte Carlo and Quasi-Monte Carlo Methods 2000,
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pp. 274-289. Springer, Berlin, Heidelberg, 2002.
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.. [25] Owen, Art B., and Seth D. Tribble. "A quasi-Monte Carlo Metropolis
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algorithm." Proceedings of the National Academy of Sciences 102,
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no. 25: 8844-8849, 2005.
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.. [26] Chen, Su. "Consistency and convergence rate of Markov chain quasi Monte
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Carlo with examples." PhD diss., Stanford University, 2011.
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.. [27] Joe, Stephen, and Frances Y. Kuo. "Constructing Sobol sequences with
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better two-dimensional projections." SIAM Journal on Scientific Computing
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30, no. 5: 2635-2654, 2008.
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"""
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from ._qmc import * # noqa: F403
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