A Symbolic Out-of-Core Solution Method for Markov Models

An Efficient Symbolic Out-of-Core Solution Method for Markov Models

An EÆcient Symbolic Out-of-Core Solution Method for Markov Models?

In recent years, disk-based approaches to the analysis of Markov models have proved to be an e ective method of combating the state space explosion problem. Coupled with parallel and symbolic techniques, disk-based methods have demonstrated impressive performance for numerical solution. In an earlier paper, we presented a novel, symbolic out-of-core algorithm which used MTBDD-based data structu...

An Efficient BDD-Based Implementation of Gauss-Seidel for CTMC Analysis

Symbolic approaches to the analysis of Markov models, i.e. those that use BDDbased data structures, have proved to be an effective method of combating the state space explosion problem. One such example is the use of offset-labelled MTBDDs (multiterminal BDDs). However, a major disadvantage of this data structure is that it cannot be used with efficient iterative methods, in particular, Gauss-S...

Serial Disk-based Analysis of Large Stochastic Models

The paper presents a survey of out-of-core methods available for the analysis of large Markov chains on single workstations. First, we discuss the main sparse matrix storage schemes and review iterative methods for the solution of systems of linear equations typically used in disk-based methods. Next, various out-of-core approaches for the steady state solution of CTMCs are described. In this c...

Applying Semi-Markov Models for forecasting the Triple Dimensions of Next Earthquake Occurrences: with Case Study in Iran Area

  In this paper Semi-Markov models are used to forecast the triple dimensions of next earthquake occurrences. Each earthquake can be investigated in three dimensions including temporal, spatial and magnitude. Semi-Markov models can be used for earthquake forecasting in each arbitrary area and each area can be divided into several zones. In Semi-Markov models each zone can be considered as a sta...