Computers & Industrial Engineering, ISSN 0360-8352, 10/2019, Volume 136, pp. 160 - 172

•The MCMC tip selection algorithm has good properties.•In the Tangle model, there are “almost symmetric” Nash equilibria.•The “selfish” players will...

Directed acyclic graph | IOTA | Primary 91A15 | Random walk | Tip selection | Nash equilibrium | Cryptocurrency | 68M14 | Secondary 60J20 | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | ENGINEERING, INDUSTRIAL

Directed acyclic graph | IOTA | Primary 91A15 | Random walk | Tip selection | Nash equilibrium | Cryptocurrency | 68M14 | Secondary 60J20 | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | ENGINEERING, INDUSTRIAL

Journal Article

Probability Surveys, ISSN 1549-5787, 2007, Volume 4, Issue 1, pp. 1 - 79

Probability Surveys 2007, Vol. 4, 1-79 The models surveyed include generalized P\'{o}lya urns, reinforced random walks, interacting urn models, and continuous...

Reinforced random walk | Urn model | Ṕolya's urn | VRRW | Dynamical system | Stochas-tic approximation | Self-avoiding walk | Agent-based model | Learning | Urn scheme | ERRW | Evo-lutionary game theory | Exchangeability | Lyapunov function | Mathematics - Probability

Reinforced random walk | Urn model | Ṕolya's urn | VRRW | Dynamical system | Stochas-tic approximation | Self-avoiding walk | Agent-based model | Learning | Urn scheme | ERRW | Evo-lutionary game theory | Exchangeability | Lyapunov function | Mathematics - Probability

Journal Article

Journal of the Royal Statistical Society. Series B, Statistical methodology, ISSN 1369-7412, 1/2014, Volume 76, Issue 1, pp. 29 - 46

Models of dynamic networks—networks that evolve over time—have manifold applications. We develop a discrete time generative model for social network evolution...

Maximum likelihood estimation | Social networks | Exponential random‐graph model | Markov chain Monte Carlo methods | Longitudinal network | Exponential random-graph model | DISCRETE TEMPORAL MODELS | EXPONENTIAL FAMILY MODELS | STATISTICS & PROBABILITY | Markov processes | Monte Carlo method | Algorithms | Analysis | Studies | Graph theory | Dynamical systems | Networks | Dynamics | Evolution | Models | Flexibility | Temporal logic | Statistics - Methodology | Exponential random graph model | Longitudinal | Markov chain Monte Carlo

Maximum likelihood estimation | Social networks | Exponential random‐graph model | Markov chain Monte Carlo methods | Longitudinal network | Exponential random-graph model | DISCRETE TEMPORAL MODELS | EXPONENTIAL FAMILY MODELS | STATISTICS & PROBABILITY | Markov processes | Monte Carlo method | Algorithms | Analysis | Studies | Graph theory | Dynamical systems | Networks | Dynamics | Evolution | Models | Flexibility | Temporal logic | Statistics - Methodology | Exponential random graph model | Longitudinal | Markov chain Monte Carlo

Journal Article

The American Mathematical Monthly, ISSN 0002-9890, 10/2018, Volume 125, Issue 9, pp. 771 - 796

Service sports include two-player contests such as volleyball, badminton, and squash. We analyze four rules, including the Standard Rule (SR), in which a...

MSC: Primary 60J20 | Secondary 91A80 | 91A20 | MATHEMATICS | ADVANTAGE

MSC: Primary 60J20 | Secondary 91A80 | 91A20 | MATHEMATICS | ADVANTAGE

Journal Article

Statistics and Computing, ISSN 0960-3174, 1/2018, Volume 28, Issue 1, pp. 131 - 144

We present a sequential Monte Carlo algorithm for Markov chain trajectories with proposals constructed in reverse time, which is advantageous when paths are...

Statistics and Computing/Statistics Programs | 60J20 | Intractable likelihood | Rare event simulation | 62M05 | Statistical Theory and Methods | Statistics | Stopped Markov process | Artificial Intelligence (incl. Robotics) | Sequential Monte Carlo | Probability and Statistics in Computer Science | Time reversal | 60J22 | DISTRIBUTIONS | STATISTICS & PROBABILITY | COMPUTER SCIENCE, THEORY & METHODS | LARGE DEVIATIONS | COALESCENT HISTORIES | Computer science | Markov processes | Algorithms | Analysis

Statistics and Computing/Statistics Programs | 60J20 | Intractable likelihood | Rare event simulation | 62M05 | Statistical Theory and Methods | Statistics | Stopped Markov process | Artificial Intelligence (incl. Robotics) | Sequential Monte Carlo | Probability and Statistics in Computer Science | Time reversal | 60J22 | DISTRIBUTIONS | STATISTICS & PROBABILITY | COMPUTER SCIENCE, THEORY & METHODS | LARGE DEVIATIONS | COALESCENT HISTORIES | Computer science | Markov processes | Algorithms | Analysis

Journal Article

Nonlinearity, ISSN 0951-7715, 08/2012, Volume 25, Issue 8, pp. 2303 - 2335

We study the stochastic FitzHugh-Nagumo equations, modelling the dynamics of neuronal action potentials in parameter regimes characterized by mixed-mode...

CHAOS | MATHEMATICS, APPLIED | SINGULAR HOPF-BIFURCATION | STATES | EXCITABLE SYSTEMS | RESONANCE | TIMES | HODGKIN-HUXLEY MODEL | NOISE | PHYSICS, MATHEMATICAL | RELAXATION OSCILLATIONS | NEURONAL MODEL | Intervals | Asymptotic properties | Mathematical analysis | Eigenvalues | Oscillations | Mathematical models | Stochasticity | Statistics | Probability | Dynamical Systems | Mathematics

CHAOS | MATHEMATICS, APPLIED | SINGULAR HOPF-BIFURCATION | STATES | EXCITABLE SYSTEMS | RESONANCE | TIMES | HODGKIN-HUXLEY MODEL | NOISE | PHYSICS, MATHEMATICAL | RELAXATION OSCILLATIONS | NEURONAL MODEL | Intervals | Asymptotic properties | Mathematical analysis | Eigenvalues | Oscillations | Mathematical models | Stochasticity | Statistics | Probability | Dynamical Systems | Mathematics

Journal Article

Journal of applied probability, ISSN 0021-9002, 06/2014, Volume 51, Issue 2, pp. 542 - 555

We characterise the class of distributions of random stochastic matrices X with the property that the products X(n)X(n − 1) · · · X(1) of independent and...

Markov chain | Products of random matrices | Limit distribution | Random exchange model | Random nested simplices | Dirichlet distribution | Service networks with polling | limit distribution | 60J20 | 60F99 | random nested simplices | 60B20 | 60J05 | random exchange model | service networks with polling

Markov chain | Products of random matrices | Limit distribution | Random exchange model | Random nested simplices | Dirichlet distribution | Service networks with polling | limit distribution | 60J20 | 60F99 | random nested simplices | 60B20 | 60J05 | random exchange model | service networks with polling

Journal Article

The Annals of Applied Probability, ISSN 1050-5164, 8/2008, Volume 18, Issue 4, pp. 1351 - 1378

Let $(X_{n}\colon n\geq 0)$ be a sequence of i.i.d. r.v.'s with negative mean. Set S₀ = 0 and define $S_{n}=X_{1}+\cdots +X_{n}$. We propose an importance...

Estimate reliability | Approximation | Algorithms | Random walk | Insurance risk | Random variables | Traffic estimation | Sampling distributions | Estimators | Probabilities | Single-server queue | Change-of-measure | Random walks | Rare-event simulation | State-dependent importance sampling | Heavy-tails | Lyapunov bounds | MONTE-CARLO | random walks | rare-event simulation | change-of-measure | STATISTICS & PROBABILITY | heavy-tails | single-server queue | state-dependent importance sampling | Mathematics - Probability | 60J20 | 68W40 | 60G50 | 60G70 | 60J05

Estimate reliability | Approximation | Algorithms | Random walk | Insurance risk | Random variables | Traffic estimation | Sampling distributions | Estimators | Probabilities | Single-server queue | Change-of-measure | Random walks | Rare-event simulation | State-dependent importance sampling | Heavy-tails | Lyapunov bounds | MONTE-CARLO | random walks | rare-event simulation | change-of-measure | STATISTICS & PROBABILITY | heavy-tails | single-server queue | state-dependent importance sampling | Mathematics - Probability | 60J20 | 68W40 | 60G50 | 60G70 | 60J05

Journal Article

Stochastic Processes and their Applications, ISSN 0304-4149, 02/2020, Volume 130, Issue 2, pp. 1041 - 1073

We study two types of Metropolis–Hastings (MH) reversiblizations for non-reversible Markov chains with Markov kernel P. While the first type is the classical...

Variance bounds | Metropolis–Hastings algorithm | Spectral gap | Non-reversible Markov chain | Mixing time | Weyl’s inequality | Weyl's inequality | STATISTICS & PROBABILITY | Metropolis-Hastings algorithm | Mathematics - Probability

Variance bounds | Metropolis–Hastings algorithm | Spectral gap | Non-reversible Markov chain | Mixing time | Weyl’s inequality | Weyl's inequality | STATISTICS & PROBABILITY | Metropolis-Hastings algorithm | Mathematics - Probability

Journal Article

10.
Full Text
General Error Estimates for the Longstaff–Schwartz Least-Squares Monte Carlo Algorithm

Mathematics of operations research, ISSN 0364-765X, 11/2019

Journal Article

The Annals of applied probability, ISSN 1050-5164, 4/2015, Volume 25, Issue 2, pp. 477 - 522

We study the SIR epidemic model with infections carried by k particles making independent random walks on a random regular graph. Here we assume k ≤ n𝜖, where...

Epidemics | Random graphs | Random walks | STATISTICS & PROBABILITY | random graphs | INFECTION | epidemics | COVER TIME | Mathematics - Probability | 60J20 | 05C81 | 05C80

Epidemics | Random graphs | Random walks | STATISTICS & PROBABILITY | random graphs | INFECTION | epidemics | COVER TIME | Mathematics - Probability | 60J20 | 05C81 | 05C80

Journal Article

Journal of Mathematical Biology, ISSN 0303-6812, 3/2019, Volume 78, Issue 4, pp. 1033 - 1065

This paper is based on the complete classification of evolutionary scenarios for the Moran process with two strategies given by Taylor et al. (Bull Math Biol...

60J20 | 91A22 | Mathematical and Computational Biology | 92D15 | Birth death processes | Markov chains | Mathematics | Applications of Mathematics | Asymptotic analysis | BIOLOGY | MATHEMATICAL & COMPUTATIONAL BIOLOGY | COOPERATION | VIEW | Usage | Analysis | Stochastic processes | Population biology | Fixation | Asymptotic properties | Classification | Probability | Population number | Evolution | Game theory

60J20 | 91A22 | Mathematical and Computational Biology | 92D15 | Birth death processes | Markov chains | Mathematics | Applications of Mathematics | Asymptotic analysis | BIOLOGY | MATHEMATICAL & COMPUTATIONAL BIOLOGY | COOPERATION | VIEW | Usage | Analysis | Stochastic processes | Population biology | Fixation | Asymptotic properties | Classification | Probability | Population number | Evolution | Game theory

Journal Article

Journal of Mathematical Biology, ISSN 0303-6812, 11/2017, Volume 75, Issue 5, pp. 1285 - 1317

Evolutionary game theory is a mathematical approach to studying how social behaviors evolve. In many recent works, evolutionary competition between strategies...

60J20 | Selection strength | Social behavior | 91A22 | Mathematical and Computational Biology | 92D15 | Moran process | Mathematics | Applications of Mathematics | Game theory | FINITE POPULATIONS | STABILITY | COOPERATION | EMERGENCE | GRAPHS | CYCLES | BIOLOGY | MATHEMATICAL & COMPUTATIONAL BIOLOGY | STRUCTURED POPULATIONS | DYNAMICS | RULES | FIXATION | Usage | Analysis | Population biology | Competition | Fixation | Mathematical analysis | Stochastic processes | Population number | Population | Evolution | Stochasticity

60J20 | Selection strength | Social behavior | 91A22 | Mathematical and Computational Biology | 92D15 | Moran process | Mathematics | Applications of Mathematics | Game theory | FINITE POPULATIONS | STABILITY | COOPERATION | EMERGENCE | GRAPHS | CYCLES | BIOLOGY | MATHEMATICAL & COMPUTATIONAL BIOLOGY | STRUCTURED POPULATIONS | DYNAMICS | RULES | FIXATION | Usage | Analysis | Population biology | Competition | Fixation | Mathematical analysis | Stochastic processes | Population number | Population | Evolution | Stochasticity

Journal Article

Journal of Applied Analysis, ISSN 1425-6908, 06/2018, Volume 24, Issue 1, pp. 99 - 107

In this paper, we investigate deficit distributions at ruin in a regime-switching Sparre Andersen model. A Markov chain is assumed to switch the amount and/or...

risk operators | NBU | 60J20 | Regime-switching Sparre Andersen model | ruin probabilities | deficit distributions at ruin | 91B30 | Mathematical analysis | Markov analysis

risk operators | NBU | 60J20 | Regime-switching Sparre Andersen model | ruin probabilities | deficit distributions at ruin | 91B30 | Mathematical analysis | Markov analysis

Journal Article

Statistics and Computing, ISSN 0960-3174, 11/2016, Volume 26, Issue 6, pp. 1213 - 1228

The classical Metropolis-Hastings (MH) algorithm can be extended to generate non-reversible Markov chains. This is achieved by means of a modification of the...

Statistics and Computing/Statistics Programs | 60J20 | Metropolis-Hastings | MCMC | Statistical Theory and Methods | Markov Chain Monte Carlo | Large deviations | Asymptotic variance | Statistics | 65C40 | Langevin sampling | Artificial Intelligence (incl. Robotics) | Non-reversible Markov processes | Probability and Statistics in Computer Science | MARKOV-CHAINS | ALGORITHM | STATISTICS & PROBABILITY | COMPUTER SCIENCE, THEORY & METHODS | Markov processes | Monte Carlo method | Algorithms | Mathematics - Probability

Statistics and Computing/Statistics Programs | 60J20 | Metropolis-Hastings | MCMC | Statistical Theory and Methods | Markov Chain Monte Carlo | Large deviations | Asymptotic variance | Statistics | 65C40 | Langevin sampling | Artificial Intelligence (incl. Robotics) | Non-reversible Markov processes | Probability and Statistics in Computer Science | MARKOV-CHAINS | ALGORITHM | STATISTICS & PROBABILITY | COMPUTER SCIENCE, THEORY & METHODS | Markov processes | Monte Carlo method | Algorithms | Mathematics - Probability

Journal Article

The Annals of Applied Probability, ISSN 1050-5164, 6/2012, Volume 22, Issue 3, pp. 881 - 930

Diffusion limits of MCMC methods in high dimensions provide a useful theoretical tool for studying computational complexity. In particular, they lead directly...

Brownian motion | Approximation | Covariance | Random walk | Metropolitan areas | Markov chains | Hilbert spaces | Coordinate systems | Random variables | Martingales | Optimal convergence time | Scaling limits | Markov chain Monte Carlo | Stochastic PDEs | optimal convergence time | stochastic PDEs | SPDES | scaling limits | APPROXIMATION | INVERSE PROBLEMS | STATISTICS & PROBABILITY | CHAINS | 60J20 | 65C40 | 65C05 | 60H15 | 60J22

Brownian motion | Approximation | Covariance | Random walk | Metropolitan areas | Markov chains | Hilbert spaces | Coordinate systems | Random variables | Martingales | Optimal convergence time | Scaling limits | Markov chain Monte Carlo | Stochastic PDEs | optimal convergence time | stochastic PDEs | SPDES | scaling limits | APPROXIMATION | INVERSE PROBLEMS | STATISTICS & PROBABILITY | CHAINS | 60J20 | 65C40 | 65C05 | 60H15 | 60J22

Journal Article

Journal of applied probability, ISSN 0021-9002, 03/2012, Volume 49, Issue 1, pp. 114 - 136

We consider the problem of twenty questions with noisy answers, in which we seek to find a target by repeatedly choosing a set, asking an oracle whether the...

Bisection | Entropy loss | Object detection | Search | Bayesian experimental design | Dynamic programming | Twenty questions | Sequential experimental design | dynamic programing | 60J20 | search | bisection | sequential experimental design | 62C10 | 90C39 | object detection | entropy loss | 90B40

Bisection | Entropy loss | Object detection | Search | Bayesian experimental design | Dynamic programming | Twenty questions | Sequential experimental design | dynamic programing | 60J20 | search | bisection | sequential experimental design | 62C10 | 90C39 | object detection | entropy loss | 90B40

Journal Article

Annals of Operations Research, ISSN 0254-5330, 12/2016, Volume 247, Issue 1, pp. 257 - 289

As a model of a service center with multiple servers and prioritized impatient customers served in reverse order of arrival such as the 9-1-1 call center in...

60J20 | Impatient customers | Theory of Computation | Last-come first-served | Abandonment | Waiting time | Multiserver | 90B22 | Business and Management | Nonpreemptive priority | Operation Research/Decision Theory | 60K25 | Combinatorics | Queue | Polite customers

60J20 | Impatient customers | Theory of Computation | Last-come first-served | Abandonment | Waiting time | Multiserver | 90B22 | Business and Management | Nonpreemptive priority | Operation Research/Decision Theory | 60K25 | Combinatorics | Queue | Polite customers

Journal Article

The Annals of Applied Probability, ISSN 1050-5164, 12/2012, Volume 22, Issue 6, pp. 2320 - 2356

The Metropolis-adjusted Langevin (MALA) algorithm is a sampling algorithm which makes local moves by incorporating information about the gradient of the...

Brownian motion | Approximation | Covariance | Random walk | Markov chains | Metropolitan areas | Hilbert spaces | Coordinate systems | Random variables | Martingales | Diffusion approximation | Metropolis-adjusted Langevin algorithm | Markov chain Monte Carlo | Scaling limit | diffusion approximation | scaling limit | WEAK-CONVERGENCE | STATISTICS & PROBABILITY | METROPOLIS | 60J20 | 65C05

Brownian motion | Approximation | Covariance | Random walk | Markov chains | Metropolitan areas | Hilbert spaces | Coordinate systems | Random variables | Martingales | Diffusion approximation | Metropolis-adjusted Langevin algorithm | Markov chain Monte Carlo | Scaling limit | diffusion approximation | scaling limit | WEAK-CONVERGENCE | STATISTICS & PROBABILITY | METROPOLIS | 60J20 | 65C05

Journal Article

Journal of Mathematical Biology, ISSN 0303-6812, 2/2018, Volume 76, Issue 3, pp. 645 - 678

Two major forces shaping evolution are drift and selection. The standard models of neutral drift—the Wright–Fisher (WF) and Moran processes—can be extended to...

60J20 | Fixation | 91A22 | Mathematical and Computational Biology | 92D15 | Selection | Evolutionary stability | Mathematics | Drift | Applications of Mathematics | Evolutionary robustness | COALESCENT PROCESSES | MARKOV-CHAINS | COOPERATION | INDIVIDUALS | SKEWED OFFSPRING DISTRIBUTION | MODELS | FIXATION PROBABILITIES | BIOLOGY | MATHEMATICAL & COMPUTATIONAL BIOLOGY | STRUCTURED POPULATIONS | DYNAMICS | Mathematical research | Evolutionary algorithms | Research | Stability | Populations | Evolution | Models | Population genetics

60J20 | Fixation | 91A22 | Mathematical and Computational Biology | 92D15 | Selection | Evolutionary stability | Mathematics | Drift | Applications of Mathematics | Evolutionary robustness | COALESCENT PROCESSES | MARKOV-CHAINS | COOPERATION | INDIVIDUALS | SKEWED OFFSPRING DISTRIBUTION | MODELS | FIXATION PROBABILITIES | BIOLOGY | MATHEMATICAL & COMPUTATIONAL BIOLOGY | STRUCTURED POPULATIONS | DYNAMICS | Mathematical research | Evolutionary algorithms | Research | Stability | Populations | Evolution | Models | Population genetics

Journal Article

No results were found for your search.

Cannot display more than 1000 results, please narrow the terms of your search.