✦ LIBER ✦

Backward jump continuous-time random walk: An application to market trading

✍ Scribed by Gubiec, Tomasz; Kutner, Ryszard

Book ID: 125885804
Publisher: The American Physical Society
Year: 2010
Tongue: English
Weight: 280 KB
Volume: 82
Category: Article
ISSN: 1063-651X
DOI: 10.1103/PhysRevE.82.046119

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Continuous time random walk model of hop

Continuous time random walk model of hopping transport: Application to impurity conduction

✍ H Scher; M Lax 📂 Article 📅 1972 🏛 Elsevier Science 🌐 English ⚖ 302 KB

Application of Continuous Time Random Wa

Application of Continuous Time Random Walks to Transport in Porous Media†

✍ Margolin, Gennady; Berkowitz, Brian 📂 Article 📅 2000 🏛 American Chemical Society 🌐 English ⚖ 98 KB

Application of Continuous Time Random Wa

Application of Continuous Time Random Walks to Transport in Porous Media

✍ Berkowitz, B. 📂 Article 📅 2000 🏛 American Chemical Society 🌐 English ⚖ 18 KB

The Random Walk [In Continuous Time] and

The Random Walk [In Continuous Time] and Its Application to the Theory of Queues

✍ C. R. Heathcote and J. E. Moyal 📂 Article 📅 1959 🏛 Oxford University Press 🌐 English ⚖ 939 KB

Continuous-time random-walk approach to

Continuous-time random-walk approach to supercooled liquids. I. Different definitions of particle jumps and their consequences

✍ Helfferich, J.; Ziebert, F.; Frey, S.; Meyer, H.; Farago, J.; Blumen, A.; Baschn 📂 Article 📅 2014 🏛 The American Physical Society 🌐 English ⚖ 566 KB

Bayesian inference for the mover–stayer

Bayesian inference for the mover–stayer model in continuous time with an application to labour market transition data

✍ Denis Fougère; Thierry Kamionka 📂 Article 📅 2003 🏛 John Wiley and Sons 🌐 English ⚖ 318 KB

## Abstract This paper presents Bayesian inference procedures for the continuous time mover–stayer model applied to labour market transition data collected in discrete time. These methods allow us to derive the probability of embeddability of the discrete‐time modelling with the continuous‐time one