# Random field

In physics and mathematics, a random field is a random function over an arbitrary domain (usually a multi-dimensional space such as $\mathbb {R} ^{n}$ ). That is, it is a function $f(x)$ that takes on a random value at each point $x\in \mathbb {R} ^{n}$ (or some other domain). It is also sometimes thought of as a synonym for a stochastic process with some restriction on its index set. That is, by modern definitions, a random field is a generalization of a stochastic process where the underlying parameter need no longer be real or integer valued "time" but can instead take values that are multidimensional vectors or points on some manifold.

## Formal definition

Given a probability space $(\Omega ,{\mathcal {F}},P)$ , an X-valued random field is a collection of X-valued random variables indexed by elements in a topological space T. That is, a random field F is a collection

$\{F_{t}:t\in T\}$

where each $F_{t}$  is an X-valued random variable.

## Examples

In its discrete version, a random field is a list of random numbers whose indices are identified with a discrete set of points in a space (for example, n-dimensional Euclidean space). More generally, the values might be defined over a continuous domain, and the random field might be thought of as a "function valued" random variable as described above. In quantum field theory the notion is even generalized to a random functional, one that takes on random value over a space of functions (see Feynman integral). Several kinds of random fields exist, among them the Markov random field (MRF), Gibbs random field, conditional random field (CRF), and Gaussian random field. An MRF exhibits the Markov property

$P(X_{i}=x_{i}|X_{j}=x_{j},i\neq j)=P(X_{i}=x_{i}|X_{j}=x_{j},j\in \partial _{i}),\,$

for each choice of values $(x_{j})_{j}$ . And each $\partial _{i}$  is the set of neighbors of $i$ . In other words, the probability that a random variable assumes a value depends on its immediate neighboring random variables. The probability of a random variable in an MRF is given by

$P(X_{i}=x_{i}|\partial _{i})={\frac {P(X_{i}=x_{i},\partial _{i})}{\sum _{k}P(X_{i}=k,\partial _{i})}},$

where the sum (can be an integral) is over the possible values of k. It is sometimes difficult to compute this quantity exactly. In 1974, Julian Besag proposed an approximation method relying on the relation between MRFs and Gibbs RFs.[citation needed]

## Applications

When used in the natural sciences, values in a random field are often spatially correlated. For example, adjacent values (i.e. values with adjacent indices) do not differ as much as values that are further apart. This is an example of a covariance structure, many different types of which may be modeled in a random field. One example is the Ising model where sometimes nearest neighbor interactions are only included as a simplification to better understand the model.

A common use of random fields is in the generation of computer graphics, particularly those that mimic natural surfaces such as water and earth.

In neuroscience, particularly in task-related functional brain imaging studies using PET or fMRI, statistical analysis of random fields are one common alternative to correction for multiple comparisons to find regions with truly significant activation.

They are also used in machine learning applications (see graphical models).

## Tensor-valued random fields

Random fields are of great use in studying natural processes by the Monte Carlo method in which the random fields correspond to naturally spatially varying properties. This leads to tensor-valued random fields in which the key role is played by a Statistical Volume Element (SVE); when the SVE becomes sufficiently large, its properties become deterministic and one recovers the representative volume element (RVE) of deterministic continuum physics. The second type of random fields that appear in continuum theories are those of dependent quantities (temperature, displacement, velocity, deformation, rotation, body and surface forces, stress, etc.).