Markov chain Monte Carlo simulation
Markov Chain Monte Carlo simulation of electric vehicle use for network integration studies
... Markov Chain Monte Carlo simulation, as a numerical approach, can be used to generate different electricity load profiles according to various EV charging ...MCMC simulation to ...
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DREAM(D): an adaptive Markov Chain Monte Carlo simulation algorithm to solve discrete, noncontinuous, and combinatorial posterior parameter estimation problems
... Existing theory and experiments prove convergence of well-constructed MCMC schemes to the appropriate limit- ing distribution under a variety of different conditions. In practice, this convergence is often observed to be ...
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The Impact of Monetary Policy on Economic Growth in Cambodia: Bayesian Approach
... This research paper aims to study the significance of monetary policy in the contribution to the economic growth of Cambodia. This study employs the data in the period of 2000-2018 consisting in total 19 years. Once the ...
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Markov Chain Monte Carlo to Study the Estimation of the Coefficient of Variation
... In this section, we report some numerical experiments performed to evaluate the behavior of the MCMC methods for different upper record samples froma Lomax distribution, different parameter val- ues, and different ...
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Speculative moves : multithreading Markov Chain Monte Carlo programs
... for a two-threaded speculative move implementation or ≈ 37 steps on a four- threaded implementation. Four thread speculative moves could therefore at best reduce the runtime of a MCMC application accepting 25% of its ...
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Non-linear Markov Chain Monte Carlo
... In the context of stochastic simulation, SIMCs can be thought of as storing modes and then allowing the algorithm to return to them in a relatively simple way. It is thus the attractive idea of being able to fully ...
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Exploring the Impact of Work Life Balance on the Employee and Organisational Growth
... how simulation methods based on Markov chain Monte Carlo (MCMC) make possible the routine Bayesian analysis of Two Phase Linear Regression ...years, simulation has become an ...
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Stochastic simulation and spatial statistics of large datasets using parallel computing
... the simulation and growth of a forest fire front. These spatial simulation models as well as spatial descriptive statistics such as Ripley’s K-function have wide applicability in spatial statistics but in ...
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Uncovering mental representations with Markov chain Monte Carlo
... Markov chain Monte Carlo is one of the basic tools in modern statistical computing, providing the basis for numerical simulations conducted in a wide range of ...numerical simulation, ...
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Comparing Markov Chain Samplers for Molecular Simulation
... Keywords: Markov chain Monte Carlo; stochastic dynamics integrators; decorrelation time; integrated.. 15.[r] ...
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Accelerating Markov chain Monte Carlo via parallel predictive prefetching
... MH simulation starts away from convergence, progresses through burn-in and eventually converges, while achieving a meaningful acceptance rate and number of effective ...
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Bayesian Estimation Using MCMC Approach Based on Progressive First-Failure Censoring from Generalized Pareto Distribution
... using Markov Chain Monte Carlo (MCMC) method to generate from the posterior distributions and in turn computing the Bayes estimators are ...a Monte Carlo simulation ...
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Parallel Markov Chain Monte Carlo
... the chain - how generally accepting the test ...the chain will shift states. ‘Heating’ a chain (by setting γ < 1) makes it more likely any arbitrary move will be accepted by the ...
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Particle Gibbs with Ancestor Sampling
... Particle Markov chain Monte Carlo (PMCMC) is a systematic way of combining the two main tools used for Monte Carlo statistical inference: sequential Monte Carlo ...
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Stability and examples of some approximate MCMC algorithms
... about Markov chains in general state spaces and the introduction of the Metropolis- Hastings ...sequential Monte Carlo methods, which will become relevant when dealing with intractabil- ...
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Metabolic characteristics and genomic epidemiology of Escherichia coli serogroup O145 : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Microbiology at Massey University, Palmerston North, New Zealand
... Markov cluster Markov Chain Monte Carlo Minute Millilitres Multi-locus sequence typing Multiplex polymerase chain reaction Modified tryptone soya broth Nanogram Nanomolar Polymerase chai[r] ...
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II. DEVELOPING A NEW ALGORITHM
... We present a non-parametric multiple imputation algorithm –GMI—for imputing missing data. The idea of the algorithm is based on the concept of GRNN. We tested our algorithms on fifteen real world datasets and thirty ...
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Probabilistic Prognostics and Health Management for Fatigue-critical Components using High-fidelity Models.
... evolution Markov Chain (DE-MC) algorithm [ 27 ] and its extensions, and the differential evolution adaptive Metropolis (DREAM) algorithms [ 134, 270 ] ...the Markov chain that is discarded and ...
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Particle Filters and Data Assimilation
... Figure 3: Left-hand column: results from the simple smoother of Kitagawa (1996) for the stochastic volatility model; right-hand column: results from the forward-backward smoother. Top plots show the paths stored after ...
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Monte Carlo methods
... distribution. Monte Carlo methods are sampling algorithms that allow to com- pute these integrals numerically when they are not analytically ...common Monte Carlo algorithms, among which ...
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