Laplace's method

In mathematics, Laplace's method, named after Pierre-Simon Laplace, is a technique used to approximate integrals of the form

$\int_a^b\! e^{M f(x)} \, dx$

where ƒ(x) is some twice-differentiable function, M is a large number, and the integral endpoints a and b could possibly be infinite. This technique was originally presented in Laplace (1774, pp. 366–367).

1 The idea of Laplace's method
2 General theory of Laplace's method
3 Laplace's method extension: Steepest descent
4 Further generalizations
5 Complex integrals
6 Example 1: Stirling's approximation
7 Example 2: parameter estimation and probabilistic inference
8 See also
9 References

The idea of Laplace's method

Assume that the function ƒ(x) has a unique global maximum at x₀. Then, the value ƒ(x₀) will be larger than other values ƒ(x). If we multiply this function by a large number M, the gap between Mƒ(x₀) and Mƒ(x) will only increase, and then it will grow exponentially for the function

$e^{M f(x)}. \,$

Thus, significant contributions to the integral of this function will come only from points x in a neighborhood of x₀, which can then be estimated.

General theory of Laplace's method

To state and motivate the method, we need several assumptions. We will assume that x₀ is not an endpoint of the interval of integration, that the values ƒ(x) cannot be very close to ƒ(x₀) unless x is close to x₀, and that the second derivative $f''(x_0)<0$ .

We can expand ƒ(x) around x₀ by Taylor's theorem,

$f(x) = f(x_0) %2B f'(x_0)(x-x_0) %2B \frac{1}{2} f''(x_0)(x-x_0)^2 %2B R$

where $R = O\left((x-x_0)^3\right).$

Since ƒ has a global maximum at x₀, and since x₀ is not an endpoint, it is a stationary point, so the derivative of ƒ vanishes at x₀. Therefore, the function ƒ(x) may be approximated to quadratic order

$f(x) \approx f(x_0) - \frac{1}{2} |f''(x_0)| (x-x_0)^2$

for x close to x₀ (recall that the second derivative is negative at the global maximum ƒ(x₀)). The assumptions made ensure the accuracy of the approximation

$\int_a^b\! e^{M f(x)}\, dx\approx e^{M f(x_0)}\int_a^b e^{-M|f''(x_0)| (x-x_0)^2/2} \, dx$

(see the picture on the right). This latter integral is a Gaussian integral if the limits of integration go from −∞ to +∞ (which can be assumed because the exponential decays very fast away from x₀), and thus it can be calculated. We find

$\int_a^b\! e^{M f(x)}\, dx\approx \sqrt{\frac{2\pi}{M|f''(x_0)|}}e^{M f(x_0)} \mbox { as } M\to\infty. \,$

A generalization of this method and extension to arbitrary precision is provided by Fog (2008).

Laplace's method extension: Steepest descent

Main article: method of steepest descent

In extensions of Laplace's method, complex analysis, and in particular Cauchy's integral formula, is used to find a contour of steepest descent for an (asymptotically with large M) equivalent integral, expressed as a line integral. In particular, if no point x₀ where the derivative of ƒ vanishes exists on the real line, it may be necessary to deform the integration contour to an optimal one, where the above analysis will be possible. Again the main idea is to reduce, at least asymptotically, the calculation of the given integral to that of a simpler integral that can be explicitly evaluated. See the book of Erdelyi (1956) for a simple discussion (where the method is termed steepest descents).

The appropriate formulation for the complex z-plane is

$\int_a^b\! e^{M f(z)}\, dz\approx \sqrt{\frac{2\pi}{-Mf''(z_0)}}e^{M f(z_0)} \mbox { as } M\to\infty. \,$

for a path passing through the saddle point at z₀. Note the explicit appearance of a minus sign to indicate the direction of the second derivative: one must not take the modulus. Also note that if the integrand is meromorphic, one may have to add residues corresponding to poles traversed while deforming the contour (see for example section 3 of Okounkov's paper Symmetric functions and random partitions).

Further generalizations

An extension of the steepest descent method is the so-called nonlinear stationary phase/steepest descent method. Here, instead of integrals, one needs to evaluate asymptotically solutions of Riemann–Hilbert factorization problems.

Given a contour C in the complex sphere, a function ƒ defined on that contour and a special point, say infinity, one seeks a function M holomorphic away from the contour C, with prescribed jump across C, and with a given normalization at infinity. If ƒ and hence M are matrices rather than scalars this is a problem that in general does not admit an explicit solution.

An asymptotic evaluation is then possible along the lines of the linear stationary phase/steepest descent method. The idea is to reduce asymptotically the solution of the given Riemann–Hilbert problem to that of a simpler, explicitly solvable, Riemann–Hilbert problem. Cauchy's theorem is used to justify deformations of the jump contour.

The nonlinear stationary phase was introduced by Deift and Zhou in 1993, based on earlier work of Its. A (properly speaking) nonlinear steepest descent method was introduced by Kamvissis, K. McLaughlin and P. Miller in 2003, based on previous work of Lax, Levermore, Deift, Venakides and Zhou.

The nonlinear stationary phase/steepest descent method has applications to the theory of soliton equations and integrable models, random matrices and combinatorics.

Complex integrals

For complex integrals in the form:

$\frac{1}{2\pi i}\int_{c-i\infty}^{c%2Bi\infty} g(s)e^{st} \,ds$

with t >> 1, we make the substitution t = iu and the change of variable s = c + ix to get the Laplace bilateral transform:

$\frac{1}{2 \pi}\int_{-\infty}^\infty g(c%2Bix)e^{-ux}e^{icu} \, dx.$

We then split g(c+ix) in its real and complex part, after which we recover u = t / i. This is useful for inverse Laplace transforms, the Perron formula and complex integration.

Example 1: Stirling's approximation

Laplace's method can be used to derive Stirling's approximation

$N!\approx \sqrt{2\pi N} N^N e^{-N}\,$

for a large integer N.

From the definition of the Gamma function, we have

$N! = \Gamma(N%2B1)=\int_0^\infty e^{-x} x^N \, dx.$

Now we change variables, letting

$x = N z \,$

so that

$dx = N dz. \,$

Plug these values back in to obtain

$\begin{align} N! & = \int_0^\infty e^{-N z} \left(N z \right)^N N \, dz \\ & = N^{N%2B1}\int_0^\infty e^{-N z} z^N \, dz \\ & = N^{N%2B1}\int_0^\infty e^{-N z} e^{N\ln z} \, dz \\ & = N^{N%2B1}\int_0^\infty e^{N(\ln z-z)} \, dz. \end{align}$

This integral has the form necessary for Laplace's method with

$f \left( z \right) = \ln{z}-z$

which is twice-differentiable:

$f'(z) = \frac{1}{z}-1,\,$

$f''(z) = -\frac{1}{z^2}.\,$

The maximum of ƒ(z) lies at z₀ = 1, and the second derivative of ƒ(z) has at this point the value −1. Therefore, we obtain

$N! \approx N^{N%2B1}\sqrt{\frac{2\pi}{N}} e^{-N}=\sqrt{2\pi N} N^N e^{-N}.\,$

Example 2: parameter estimation and probabilistic inference

Azevedo-Filho and Shachter (1994) reviews Laplace's method results (univariate and multivariate) and presents a detailed example showing the method used in parameter estimation and probabilistic inference under a bayesian perspective. Laplace's method is applied to a meta-analysis problem from the medical domain, involving experimental data, and compared to other techniques. (article)

References

Azevedo-Filho, A.; Shachter, R. (1994), "Laplace's Method Approximations for Probabilistic Inference in Belief Networks with Continuous Variables", in Mantaras, R.; Poole, D., Uncertainty in Artificial Intelligence, San Francisco, CA: Morgan Kauffman, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.91.2064&rep=rep1&type=pdf .
Deift, P.; Zhou, X. (1993), "A steepest descent method for oscillatory Riemann–Hilbert problems. Asymptotics for the MKdV equation", Ann. of Math. 137 (2): 295–368, doi:10.2307/2946540 .
Erdelyi, A. (1956), Asymptotic Expansions, Dover .
Fog, A. (2008), "Calculation Methods for Wallenius' Noncentral Hypergeometric Distribution", Communications in Statistics, Simulation and Computation 37 (2): 258–273, doi:10.1080/03610910701790269 .
Kamvissis, S.; McLaughlin, K. T.-R.; Miller, P. (2003), "Semiclassical Soliton Ensembles for the Focusing Nonlinear Schrödinger Equation", Annals of Mathematics Studies (Princeton University Press) 154 .
Laplace, P. S. (1774). Memoir on the probability of causes of events. Mémoires de Mathématique et de Physique, Tome Sixième. (English translation by S. M. Stigler 1986. Statist. Sci., 1(19):364–378).

This article incorporates material from saddle point approximation on PlanetMath, which is licensed under the Creative Commons Attribution/Share-Alike License.