Jump diffusion

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Jump diffusion is a stochastic process that involves jumps and diffusion. It has important applications in magnetic reconnection, coronal mass ejections, condensed matter physics, option pricing, and pattern theory and computational vision.

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In physics

In crystals, atomic diffusion typically consists of jumps between vacant lattice sites. On time and length scales that average over many single jumps, the net motion of the jumping atoms can be described as regular diffusion.

Jump diffusion can be studied on a microscopic scale by inelastic neutron scattering and by Mößbauer spectroscopy. Closed expressions for the autocorrelation function have been derived for several jump(-diffusion) models:

In economics and finance

A jump-diffusion model is a form of mixture model, mixing a jump process and a diffusion process. In finance, jump-diffusion models were first introduced by Robert C. Merton [6] . Such models have a range of financial applications from option pricing, to credit risk, to time series forecasting [7] .

In pattern theory, computer vision, and medical imaging

In pattern theory and computational vision in medical imaging, jump-diffusion processes were first introduced by Grenander and Miller [8] as a form of random sampling algorithm that mixes "focus"-like motions, the diffusion processes, with saccade-like motions, via jump processes. The approach modelled sciences of electron-micrographs as containing multiple shapes, each having some fixed dimensional representation, with the collection of micrographs filling out the sample space corresponding to the unions of multiple finite-dimensional spaces. Using techniques from pattern theory, a posterior probability model was constructed over the countable union of sample space; this is therefore a hybrid system model, containing the discrete notions of object number along with the continuum notions of shape. The jump-diffusion process was constructed to have ergodic properties so that after initially flowing away from its initial condition it would generate samples from the posterior probability model.

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Related Research Articles

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<span class="mw-page-title-main">Scattering</span> Range of physical processes

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In condensed-matter physics, a primary knock-on atom (PKA) is an atom that is displaced from its lattice site by irradiation; it is, by definition, the first atom that an incident particle encounters in the target. After it is displaced from its initial lattice site, the PKA can induce the subsequent lattice site displacements of other atoms if it possesses sufficient energy, or come to rest in the lattice at an interstitial site if it does not.

In condensed matter physics, a quantum spin liquid is a phase of matter that can be formed by interacting quantum spins in certain magnetic materials. Quantum spin liquids (QSL) are generally characterized by their long-range quantum entanglement, fractionalized excitations, and absence of ordinary magnetic order.

Peter Grassberger is a retired professor who worked in statistical and particle physics. He made contributions to chaos theory, where he introduced the idea of correlation dimension, a means of measuring a type of fractal dimension of the strange attractor.

Quasielastic neutron scattering (QENS) designates a limiting case of inelastic neutron scattering, characterized by energy transfers being small compared to the incident energy of the scattered particles. In a more strict meaning, it denotes scattering processes where dynamics in the sample lead to a broadening of the incident neutron spectrum, in contrast to, e.g., the scattering from a diffusionless crystal, where the scattered neutron energy spectrum consists of an elastic line and a number of well-separated inelastic lines due to the creation or annihilation of phonons with specific energies.

In probability theory, a piecewise-deterministic Markov process (PDMP) is a process whose behaviour is governed by random jumps at points in time, but whose evolution is deterministically governed by an ordinary differential equation between those times. The class of models is "wide enough to include as special cases virtually all the non-diffusion models of applied probability." The process is defined by three quantities: the flow, the jump rate, and the transition measure.

In mathematics, a continuous-time random walk (CTRW) is a generalization of a random walk where the wandering particle waits for a random time between jumps. It is a stochastic jump process with arbitrary distributions of jump lengths and waiting times. More generally it can be seen to be a special case of a Markov renewal process.

Varley Fullerton Sears was a Canadian physicist, notable for his contributions to the methodological foundations of neutron scattering.

References

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  3. Sears, V. F. (1966). "Theory of Cold Neutron Scattering by Homonuclear Diatomic Liquids: I. Free Rotation". Canadian Journal of Physics. 44 (6): 1279–1297. doi:10.1139/p66-108.
  4. Sears, V. F. (1967). "Cold Neutron Scattering by Molecular Liquids: Iii. Methane". Canadian Journal of Physics. 45 (2): 237–254. doi:10.1139/p67-025.
  5. Hall, P. L.; Ross, D. K. (1981). "Incoherent neutron scattering functions for random jump diffusion in bounded and infinite media". Molecular Physics. 42 (3): 673. doi:10.1080/00268978100100521.
  6. Merton, R. C. (1976). "Option pricing when underlying stock returns are discontinuous". Journal of Financial Economics . 3 (1–2): 125–144. doi:10.1016/0304-405X(76)90022-2. hdl: 1721.1/1899 .
  7. Christensen, H. L. (2012). "Forecasting high-frequency futures returns using online Langevin dynamics". IEEE Journal of Selected Topics in Signal Processing . 6 (4): 366–380. doi:10.1109/JSTSP.2012.2191532. hdl: 10.1109/JSTSP.2012.2191532 .
  8. Grenander, U.; Miller, M.I. (1994). "Representations of Knowledge in Complex Systems". Journal of the Royal Statistical Society, Series B. 56 (4): 549–603. JSTOR   2346184.