Pseudorandom numbers for modelling Markov chains
โ Scribed by N.N. Chentsov
- Publisher
- Elsevier Science
- Year
- 1967
- Weight
- 934 KB
- Volume
- 7
- Category
- Article
- ISSN
- 0041-5553
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๐ SIMILAR VOLUMES
Let P be the transition matrix for an n-state, homogeneous, ergodic Markov chain. Set Q = I -P and let Q # = [q # i,j ] be the group (generalized) inverse of Q. A well-known condition number, due to Funderlic and Meyer, which is used in the error analysis for the computation of the stationary distri
For a discrete-time Markov chain with finite state space [1, ..., r] we consider the joint distribution of the numbers of visits in states 1, ..., r&1 during the first N steps or before the N th visit to r. From the explicit expressions for the corresponding generating functions we obtain the limiti
## Abstract Understanding and quantifying the behaviour of extreme wind speeds has important applications for design in civil engineering. As in the extremal analysis of any environmental process, estimates are often required of the probability of events that are rarer than those already recorded.
A previous article [Comput. Phys. Commun. 75 (1993) 105] compared and analyzed the pseudorandom number generators that are delivered with off-the-shelf Fortran compilers for personal computers. Since the writing of that article, Microsoft has released a new 32-bit protected mode compiler which inclu