Modeling Rare Transition Events

The Effect of Rare Events on the Evaluation and Decoding of Hidden non-Markovian Models

Hidden non-Markovian Models (HnMM) are a modeling paradigm based on Hidden Markov Models. They extend the existing Hidden Markov Models by changing the hidden model from a memoryless discrete-time Markov chain to a more flexible discrete stochastic model involving time dependent transition rates. HnMM enable the analysis of not completely observable real systems only based on their interaction ...

Investigating Rare Events by Transition Interface Sampling

We briefly review simulation schemes for the investigation of rare transitions and we resume the recently introduced Transition Interface Sampling, a method in which the computation of rate constants is recast into the computation of fluxes through interfaces dividing the reactant and product state. PACS: 82.20.Db, 82.20.Sb keywords: Rare events; Rate constants; Transition Path Sampling. ∗Corre...

Transition-path theory and path-finding algorithms for the study of rare events.

Transition-path theory is a theoretical framework for describing rare events in complex systems. It can also be used as a starting point for developing efficient numerical algorithms for analyzing such rare events. Here we review the basic components of transition-path theory and path-finding algorithms. We also discuss connections with the classical transition-state theory.

A New Nonlinear Specification of Structural Breaks for Money Demand in Iran

In a structural time series regression model, binary variables have been used to quantify qualitative or categorical quantitative events such as politic and economic structural breaks, regions, age groups and etc. The use of the binary dummy variables is not reasonable because the effect of an event decreases (increases) gradually over time not at once. The simple and basic idea in this paper i...

Application of Importance Sampling to Transition Class Models with Rare Events