Results 291 to 300 of about 836,551 (359)

Parameter inference for stochastic reaction models of ion channel gating from whole-cell voltage-clamp data. [PDF]

Philos Trans A Math Phys Eng Sci
Del Core L, Mirams GR.
europepmc   +1 more source

A Two-State Random Walk Model of Sperm Search on Confined Domains. [PDF]

Entropy (Basel)
Bier M, Majka M, Schmidt C.
europepmc   +1 more source

Exploring focal adhesion data: dynamic parameter extraction from FRAP and FLAP experiments using chemical master equation. [PDF]

Front Mol Biosci
de Oliveira LR, Fernandes MG, Patane JSL, Schwartz JM, Krieger JE, Ballestrem C, Miyakawa AA.   +6 more
europepmc   +1 more source

Closing the ODE-SDE gap in score-based diffusion models through the Fokker-Planck equation. [PDF]

Philos Trans A Math Phys Eng Sci
Deveney T, Stanczuk J, Kreusser L, Budd C, Schönlieb CB.   +4 more
europepmc   +1 more source

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Stochastic Differential Equations

1985
We now return to the possible solutions X t (ω) of the stochastic differential equation (5.1) where W t is 1-dimensional “white noise”. As discussed in Chapter III the Ito interpretation of (5.1) is that X t satisfies the stochastic integral equation or in differential form (5.2) .
B. Øksendal
openaire   +3 more sources

Stochastic Differential Equations

2014
Stochastic differential equations describe the time evolution of certain continuous n-dimensional Markov processes. In contrast with classical differential equations, in addition to the derivative of the function, there is a term that describes the random fluctuations that are coded as an Ito integral with respect to a Brownian motion. Depending on how
Etienne Pardoux, Aurel Răşcanu
  +10 more sources

Stochastic Differential Equations [PDF]

, 1990
A diffusion can be thought of as a strong Markov process (in ℝn) with continuous paths. Before the development of Ito’s theory of stochastic integration for Brownian motion, the primary method of studying diffusions was to study their transition semigroups.
R. J. Williams, K. L. Chung
  +6 more sources

Stochastic Differential Equations

2012
This chapter represents the core of the book. Building on the general theory introduced in previous chapters, stochastic differential equations (SDEs) are presented as a key mathematical tool for relating the subject of dynamical systems to Wiener noise.
Vincenzo Capasso, David Bakstein
openaire   +4 more sources