A formula for singular perturbations of Markov chains

1994 ◽  
Vol 31 (3) ◽  
pp. 829-833 ◽  
Author(s):  
Jean B. Lasserre
Keyword(s):  
Markov Chain ◽  
Steady State ◽  
Probability Distribution ◽  
Markov Chains ◽  
Singular Perturbations ◽  
Singularly Perturbed ◽  
State Probability ◽  
Regular Perturbation ◽  
Steady State Probability ◽  
Fundamental Matrices

We give formulas for updating both the steady-state probability distribution and the fundamental matrices of a singularly perturbed Markov chain. This formula generalizes Schweitzer's regular perturbation formulas to the case of singular perturbations.

A formula for singular perturbations of Markov chains

1994 ◽  
Vol 31 (03) ◽  
pp. 829-833 ◽  
Author(s):  
Jean B. Lasserre
Keyword(s):  
Markov Chain ◽  
Steady State ◽  
Probability Distribution ◽  
Markov Chains ◽  
Singular Perturbations ◽  
Singularly Perturbed ◽  
State Probability ◽  
Regular Perturbation ◽  
Steady State Probability ◽  
Fundamental Matrices

We give formulas for updating both the steady-state probability distribution and the fundamental matrices of a singularly perturbed Markov chain. This formula generalizes Schweitzer's regular perturbation formulas to the case of singular perturbations.

scholarly journals Estimation of extreme inter-day changes to peak electricity demand using Markov chain analysis: A comparative analysis with extreme value theory

2017 ◽  
Vol 28 (4) ◽  
Author(s):  
Caston Sigauke ◽  
Delson Chikobvu
Keyword(s):  
Markov Chain ◽  
Steady State ◽  
Return Period ◽  
Electricity Demand ◽  
State Probability ◽  
Markov Chain Analysis ◽  
State Problem ◽  
Steady State Probability ◽  
Chain Analysis ◽  
Steady State Probabilities

Uncertainty in electricity demand is caused by many factors. Large changes are usually attributed to extreme weather conditions and the general random usage of electricity by consumers. More understanding requires a detailed analysis using a stochastic process approach. This paper presents a Markov chain analysis to determine stationary distributions (steady state probabilities) of large daily changes in peak electricity demand. Such large changes pose challenges to system operators in the scheduling and dispatching of electrical energy to consumers. The analysis used on South African daily peak electricity demand data from 2000 to 2011 and on a simple two-state discrete-time Markov chain modelling framework was adopted to estimate steady-state probabilities of two states: positive inter-day changes (increases) and negative inter-day changes (decreases). This was extended to a three-state Markov chain by distinguishing small positive changes and extreme large positive changes. For the negative changes, a decrease state was defined. Empirical results showed that the steady state probability for an increase was 0.4022 for the two-state problem, giving a return period of 2.5 days. For the three state problem, the steady state probability of an extreme increase was 0.0234 with a return period of 43 days, giving approximately nine days in a year that experience extreme inter-day increases in electricity demand. Such an analysis was found to be important for planning, load shifting, load flow analysis and scheduling of electricity, particularly during peak periods.

A kind of dual form for coupling from the past algorithm, to sample from Markov chain steady-state probability

2019 ◽  
Vol 25 (4) ◽  
pp. 317-327
Author(s):  
Abdelaziz Nasroallah ◽  
Mohamed Yasser Bounnite
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Steady State ◽  
Performance Comparison ◽  
State Probability ◽  
Steady State Probability ◽  
Dual Form ◽  
The Past ◽  
Coupling From The Past ◽  
History Of

Abstract The standard coupling from the past (CFTP) algorithm is an interesting tool to sample from exact Markov chain steady-state probability. The CFTP detects, with probability one, the end of the transient phase (called burn-in period) of the chain and consequently the beginning of its stationary phase. For large and/or stiff Markov chains, the burn-in period is expensive in time consumption. In this work, we propose a kind of dual form for CFTP called D-CFTP that, in many situations, reduces the Monte Carlo simulation time and does not need to store the history of the used random numbers from one iteration to another. A performance comparison of CFTP and D-CFTP will be discussed, and some numerical Monte Carlo simulations are carried out to show the smooth running of the proposed D-CFTP.

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