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In this example, the Gauss–Newton algorithm will be used to fit a model to some data by minimizing the sum of squares of errors between the data and model's predictions. In a biology experiment studying the relation between substrate concentration [S] and reaction rate in an enzyme-mediated reaction, the data in the following table were obtained.
The gauss is the unit of magnetic flux density B in the system of Gaussian units and is equal to Mx/cm 2 or g/Bi/s 2, while the oersted is the unit of H-field. One tesla (T) corresponds to 10 4 gauss, and one ampere (A) per metre corresponds to 4π × 10 −3 oersted.
Magnetic induction B (also known as magnetic flux density) has the SI unit tesla [T or Wb/m 2]. [1] One tesla is equal to 10 4 gauss. Magnetic field drops off as the inverse cube of the distance ( 1 / distance 3 ) from a dipole source. Energy required to produce laboratory magnetic fields increases with the square of magnetic field. [2]
Successive over-relaxation (SOR) — a technique to accelerate the Gauss–Seidel method Symmetric successive over-relaxation (SSOR) — variant of SOR for symmetric matrices; Backfitting algorithm — iterative procedure used to fit a generalized additive model, often equivalent to Gauss–Seidel; Modified Richardson iteration
The tesla (symbol: T) is the unit of magnetic flux density (also called magnetic B-field strength) in the International System of Units (SI). One tesla is equal to one weber per square metre .
In the CGS system, the unit of the H-field is the oersted and the unit of the B-field is the gauss. In the SI system, the unit ampere per meter (A/m), which is equivalent to newton per weber, is used for the H-field and the unit of tesla is used for the B-field. [3]
[29] [30] The underlying rationale of such a learning framework consists in the assumption that a given mapping cannot be well captured by a single Gaussian process model. Instead, the observation space is divided into subsets, each of which is characterized by a different mapping function; each of these is learned via a different Gaussian ...
The notation used in this section is the same as the notation used below to derive the correspondence between NNGPs and fully connected networks, and more details can be found there. The figure to the right plots the one-dimensional outputs z L ( ⋅ ; θ ) {\displaystyle z^{L}(\cdot ;\theta )} of a neural network for two inputs x ...