On short timescales, earthquakes cluster in both time and space, eventually complicating the analysis of seismicity. One basic goal is to partition the earthquake catalogue into two classes of events — background events, regarded as spontaneous or independent earthquakes, and clustered events, including events triggered by other earthquakes.
About Mark Buchanan
This author has yet to write their bio.Meanwhile lets just say that we are proud Mark Buchanan contributed a whooping 79 entries.
Entries by Mark Buchanan
Here are links to a few recent articles by LML External Fellow Mark Buchanan.
The theory of random networks is useful in modelling systems of many interacting units, ranging from neurons in the brain and computers and routers in the Internet to species in an ecosystem. In this theory, a key mathematical quantity is the eigenvalue spectrum of the adjacency matrix, the entries of which reflect the connections between […]
In recent decades, the frequency of extreme snowfall events – often entailing considerable human and economic costs – has remained mostly unchanged, despite consistently rising global temperatures.
The mathematical concepts of randomness were first developed in economics in the 17th century, primarily in the context of problems of gambling and games of chance.
A highly-focused laser beam can be used to trap microscopic particles. In this technique – known as optical tweezers – forces arise near the focal spot due to radiation pressure of the light beam (acting along the beam direction) and gradient forces which pull the particle towards the high-intensity focal spot.
Along with colleagues Jorge Velasco-Hernandez and David Sanders, LML External Fellow Isaac Pérez Castillo has organised a series of seminars to be held throughout the rest of the year on topics linked to the COVID-19 pandemic.
The seismic quiescence hypothesis asserts that the number of small earthquakes decreases in and around the focal area of a great earthquake near to its time of occurrence.
Hidden Markov models (HMMs) were first introduced in the late 1960s, and later applied widely in areas including speech recognition, bioinformatics, finance and seismology.
Recurrent neural networks (RNNs) are non-autonomous dynamical systems driven by input, the behaviour of which depend on both model parameters and inputs to the system.
London Mathematical Laboratory
8 Margravine Gardens
Risk Preferences in Time Lotteries: Seminar by Mark Kirstein
6th October 2020
Ergodicity Economics 2021 Online Conference
18 – 20 January 2021