2`.

Scrambled nets are a kind of RQMC that have some valuable robustness
properties [12]_. If the integrand is square integrable, they give variance
:math:`var_{SNET} = o(1/n)`. There is a finite upper bound on
:math:`var_{SNET} / var_{MC}` that holds simultaneously for every square
integrable integrand. Scrambled nets satisfy a strong law of large numbers
for :math:`f` in :math:`L^p` when :math:`p>1`. In some
special cases there is a central limit theorem [13]_. For smooth enough
integrands they can achieve RMSE nearly :math:`O(n^{-3})`. See [12]_
for references about these properties.

The main kinds of QMC methods are lattice rules [14]_ and digital
nets and sequences [2]_, [15]_. The theories meet up in polynomial
lattice rules [16]_ which can produce digital nets. Lattice rules
require some form of search for good constructions. For digital
nets there are widely used default constructions.

The most widely used QMC methods are Sobol' sequences [17]_.
These are digital nets. They are extensible in both :math:`n` and :math:`d`.
They can be scrambled. The special sample sizes are powers
of 2. Another popular method are Halton sequences [18]_.
The constructions resemble those of digital nets. The earlier
dimensions have much better equidistribution properties than
later ones. There are essentially no special sample sizes.
They are not thought to be as accurate as Sobol' sequences.
They can be scrambled. The nets of Faure [19]_ are also widely
used. All dimensions are equally good, but the special sample
sizes grow rapidly with dimension :math:`d`. They can be scrambled.
The nets of Niederreiter and Xing [20]_ have the best asymptotic
properties but have not shown good empirical performance [21]_.

Higher order digital nets are formed by a digit interleaving process
in the digits of the constructed points. They can achieve higher
levels of asymptotic accuracy given higher smoothness conditions on :math:`f`
and they can be scrambled [22]_. There is little or no empirical work
showing the improved rate to be attained.

Using QMC is like using the entire period of a small random
number generator. The constructions are similar and so
therefore are the computational costs [23]_.

(R)QMC is sometimes improved by passing the points through
a baker's transformation (tent function) prior to using them.
That function has the form :math:`1-2|x-1/2|`. As :math:`x` goes from 0 to
1, this function goes from 0 to 1 and then back. It is very
useful to produce a periodic function for lattice rules [14]_,
and sometimes it improves the convergence rate [24]_.

It is not straightforward to apply QMC methods to Markov
chain Monte Carlo (MCMC).  We can think of MCMC as using
:math:`n=1` point in :math:`[0,1]^{d}` for very large :math:`d`, with
ergodic results corresponding to :math:`d \to \infty`. One proposal is
in [25]_ and under strong conditions an improved rate of convergence
has been shown [26]_.

Returning to Sobol' points: there are many versions depending
on what are called direction numbers. Those are the result of
searches and are tabulated. A very widely used set of direction
numbers come from [27]_. It is extensible in dimension up to
:math:`d=21201`.

References
----------
.. [1] Owen, Art B. "Monte Carlo Book: the Quasi-Monte Carlo parts." 2019.
.. [2] Niederreiter, Harald. "Random number generation and quasi-Monte Carlo
   methods." Society for Industrial and Applied Mathematics, 1992.
.. [3] Dick, Josef, Frances Y. Kuo, and Ian H. Sloan. "High-dimensional
   integration: the quasi-Monte Carlo way." Acta Numerica no. 22: 133, 2013.
.. [4] Aho, A. V., C. Aistleitner, T. Anderson, K. Appel, V. Arnol'd, N.
   Aronszajn, D. Asotsky et al. "W. Chen et al.(eds.), "A Panorama of
   Discrepancy Theory", Sringer International Publishing,
   Switzerland: 679, 2014.
.. [5] Hickernell, Fred J. "Koksma-Hlawka Inequality." Wiley StatsRef:
   Statistics Reference Online, 2014.
.. [6] Owen, Art B. "On dropping the first Sobol' point." :arxiv:`2008.08051`,
   2020.
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.. [8] DiCiccio, Thomas J., and Bradley Efron. "Bootstrap confidence
   intervals." Statistical science: 189-212, 1996.
.. [9] Dimov, Ivan T. "Monte Carlo methods for applied scientists." World
   Scientific, 2008.
.. [10] Caflisch, Russel E., William J. Morokoff, and Art B. Owen. "Valuation
   of mortgage backed securities using Brownian bridges to reduce effective
   dimension." Journal of Computational Finance: no. 1 27-46, 1997.
.. [11] Sloan, Ian H., and Henryk Wozniakowski. "When are quasi-Monte Carlo
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   14, no. 1 (1998): 1-33.
.. [12] Owen, Art B., and Daniel Rudolf, "A strong law of large numbers for
   scrambled net integration." SIAM Review, to appear.
.. [13] Loh, Wei-Liem. "On the asymptotic distribution of scrambled net
   quadrature." The Annals of Statistics 31, no. 4: 1282-1324, 2003.
.. [14] Sloan, Ian H. and S. Joe. "Lattice methods for multiple integration."
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.. [15] Dick, Josef, and Friedrich Pillichshammer. "Digital nets and sequences:
   discrepancy theory and quasi-Monte Carlo integration." Cambridge University
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.. [16] Dick, Josef, F. Kuo, Friedrich Pillichshammer, and I. Sloan.
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.. [17] Sobol', Il'ya Meerovich. "On the distribution of points in a cube and
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   i Matematicheskoi Fiziki 7, no. 4: 784-802, 1967.
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.. [20] Niederreiter, Harold, and Chaoping Xing. "Low-discrepancy sequences and
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.. [24] Hickernell, Fred J. "Obtaining O (N-2+e) Convergence for Lattice
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   pp. 274-289. Springer, Berlin, Heidelberg, 2002.
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   better two-dimensional projections." SIAM Journal on Scientific Computing
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