Über die analytischen Methoden in der Wahrscheinlichkeitsrechnung

By A. Kolmogoroff. 1931

Subfields: Probabilistic & Bayesian Methods

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Citations in the corpus

ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis (2021, methodology): “For the diffusion process, previous image models [62, 26, 65, 27] relied on the fact that, in the limit β t → 0 , the form of the true reverse process has the same functional form as the forward diffusion process [62, 38].”
Cancer Evolution: Mathematical Models and Computational Inference (2014, methodology): “It is given by the master equation, also called Kolmogorov forward, or Fokker–Planck equation (Fisher 1922; Kolmogorov 1931; Wright 1945; Feller 1951), for the probability density (x,t) of the relative allele frequency x at time t in an evolutionary Markov process X(t), as”
Distribution-Dependent SDEs for Landau Type Equations (2016, methodology): “1 Introduction A fundamental application of the Itô SDE is to solve Kolmogorov’s problem [15] of determining Markov processes whose distribution density satisfies the Fokker-Planck-Kolmogorov equation.”
Model-Checking Algorithms for Continuous-Time Markov Chains (2002, background): “CONTINUOUS-TIMEMarkov chains (CTMCs) [34], [46], [50], [53], [69] are an important class of stochastic processes that have been widely used in practice to determine system performance and dependability characteristics.”
Algorithmic Complexity of Financial Motions (2014, unknown): “…the works of Black and Scholes (1973) on the valuation of options are largely grounded in premises that can be directly linked to Bachelier) and in Mathematics (for instance, Courtault et al. (2000) even cites Bachelier as an inspiration for the works of Kolmogorov (1931) on diffusion processes ).”
Loss of regularity for Kolmogorov equations (2012, background): “It has a strong link to probability theory and appeared first (in a slightly different form; see display (125) in [44]) in Kolmogorov’s celebrated paper [44].”