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Diagram of a restricted Boltzmann machine with three visible units and four hidden units (no bias units) A restricted Boltzmann machine (RBM) (also called a restricted Sherrington–Kirkpatrick model with external field or restricted stochastic Ising–Lenz–Little model) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.
The elements of the spatial weight matrix are determined by setting = for all connected pairs of nodes with all the other elements set to 0. This makes the spatial weight matrix equivalent to the adjacency matrix of the corresponding network. It is common [2] to row-normalize the matrix ,
In statistical mechanics, multiplicity (also called statistical weight) refers to the number of microstates corresponding to a particular macrostate of a thermodynamic system. [1]
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As a rule of thumb there should be 20 or more particles per cubic mean free path for accurate results. [citation needed] The evolution of the system is integrated in time steps, , which are typically on the order of the mean collision time for a particle. At each time step all the particles are moved and then a random set of pairs collide.
This is not a restricted Boltzmann machine. A Boltzmann machine (also called Sherrington–Kirkpatrick model with external field or stochastic Ising model), named after Ludwig Boltzmann, is a spin-glass model with an external field, i.e., a Sherrington–Kirkpatrick model, [1] that is a stochastic Ising model.
(B) (,) of weight = and minimal order exist if is a prime power and such a circulant weighing matrix can be obtained by signing the complement of a finite projective plane. Since all C W ( n , k ) {\displaystyle CW(n,k)} for k ≤ 25 {\displaystyle k\leq 25} have been classified, the first open case is C W ( 105 , 36 ) {\displaystyle CW(105,36)} .
Lattice Boltzmann models can be operated on a number of different lattices, both cubic and triangular, and with or without rest particles in the discrete distribution function. A popular way of classifying the different methods by lattice is the D n Q m scheme.