Certain boundary functionals for Markov random walks

1978 ◽  
Vol 23 (2) ◽  
pp. 169-175
Author(s):  
V. A. Ivanov ◽  
G. I. Ivchenko
Keyword(s):  
Random Walks ◽  
Markov Random ◽  
Boundary Functionals

scholarly journals Rare-Event Simulation of Heavy-Tailed Random Walks by Sequential Importance Sampling and Resampling

2012 ◽  
Vol 44 (04) ◽  
pp. 1173-1196
Author(s):  
Hock Peng Chan ◽  
Shaojie Deng ◽  
Tze-Leung Lai
Keyword(s):  
Random Walks ◽  
Importance Sampling ◽  
Rare Event ◽  
Sequential Importance Sampling ◽  
New Approach ◽  
Martingale Representation ◽  
Event Simulation ◽  
Markov Random ◽  
Heavy Tailed ◽  
Asymptotically Efficient

We introduce a new approach to simulating rare events for Markov random walks with heavy-tailed increments. This approach involves sequential importance sampling and resampling, and uses a martingale representation of the corresponding estimate of the rare-event probability to show that it is unbiased and to bound its variance. By choosing the importance measures and resampling weights suitably, it is shown how this approach can yield asymptotically efficient Monte Carlo estimates.

Asymptotic expansions in multidimensional Markov renewal theory and first passage times for Markov random walks

2001 ◽  
Vol 33 (3) ◽  
pp. 652-673 ◽  
Author(s):  
Cheng-Der Fuh ◽  
Tze Leung Lai
Keyword(s):  
Random Walks ◽  
First Passage Time ◽  
Renewal Theory ◽  
Passage Time ◽  
Boundary Crossing ◽  
High Threshold ◽  
Renewal Theorem ◽  
First Passage ◽  
Markov Random ◽  
General State Space

We prove a d-dimensional renewal theorem, with an estimate on the rate of convergence, for Markov random walks. This result is applied to a variety of boundary crossing problems for a Markov random walk (Xn,Sn), n ≥0, in which Xn takes values in a general state space and Sn takes values in ℝd. In particular, for the case d = 1, we use this result to derive an asymptotic formula for the variance of the first passage time when Sn exceeds a high threshold b, generalizing Smith's classical formula in the case of i.i.d. positive increments for Sn. For d > 1, we apply this result to derive an asymptotic expansion of the distribution of (XT,ST), where T = inf { n : Sn,1 > b } and Sn,1 denotes the first component of Sn.

Multispin Monte Carlo Method

2020 ◽  
Vol 20 (2) ◽  
pp. 212-220
Author(s):  
K.V. Makarova ◽  
◽  
A.G. Makarov ◽  
M.A. Padalko ◽  
V.S. Strongin ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Ising Model ◽  
Random Walks ◽  
State Space ◽  
Low Energy ◽  
Cluster Method ◽  
Markov Random ◽  
Statistical Equivalence ◽  
Statistical Sample

The article offers a Monte Carlo cluster method for numerically calculating a statistical sample of the state space of vector models. The statistical equivalence of subsystems in the Ising model and quasi-Markov random walks can be used to increase the efficiency of the algorithm for calculating thermodynamic means. The cluster multispin approach extends the computational capabilities of the Metropolis algorithm and allows one to find configurations of the ground and low-energy states.

scholarly journals Rare-Event Simulation of Heavy-Tailed Random Walks by Sequential Importance Sampling and Resampling

2012 ◽  
Vol 44 (4) ◽  
pp. 1173-1196 ◽  
Author(s):  
Hock Peng Chan ◽  
Shaojie Deng ◽  
Tze-Leung Lai
Keyword(s):  
Random Walks ◽  
Importance Sampling ◽  
Rare Event ◽  
Sequential Importance Sampling ◽  
New Approach ◽  
Martingale Representation ◽  
Event Simulation ◽  
Markov Random ◽  
Heavy Tailed ◽  
Asymptotically Efficient

We introduce a new approach to simulating rare events for Markov random walks with heavy-tailed increments. This approach involves sequential importance sampling and resampling, and uses a martingale representation of the corresponding estimate of the rare-event probability to show that it is unbiased and to bound its variance. By choosing the importance measures and resampling weights suitably, it is shown how this approach can yield asymptotically efficient Monte Carlo estimates.

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