Relevant bibliographies by topics / Monte Carlo Metropolis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Monte Carlo Metropolis'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Monte Carlo Metropolis"

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Owen, A. B., and S. D. Tribble. "A quasi-Monte Carlo Metropolis algorithm." Proceedings of the National Academy of Sciences 102, no. 25 (June 13, 2005): 8844–49. http://dx.doi.org/10.1073/pnas.0409596102.

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Makarova, K. V., A. G. Makarov, M. A. Padalko, V. S. Strongin, and K. V. Nefedev. "Multispin Monte Carlo Method." Dal'nevostochnyi Matematicheskii Zhurnal 20, no. 2 (November 25, 2020): 212–20. http://dx.doi.org/10.47910/femj202020.

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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.
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da Silva, Cesar R. S. "Optimizing Metropolis Monte Carlo simulations of semiconductors." Computer Physics Communications 153, no. 3 (July 2003): 392–96. http://dx.doi.org/10.1016/s0010-4655(03)00225-x.

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Rensburg, E. J. Janse van, and N. Madras. "Metropolis Monte Carlo simulation of lattice animals." Journal of Physics A: Mathematical and General 30, no. 23 (December 7, 1997): 8035–66. http://dx.doi.org/10.1088/0305-4470/30/23/007.

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Mathé, Peter, and Erich Novak. "Simple Monte Carlo and the Metropolis algorithm." Journal of Complexity 23, no. 4-6 (August 2007): 673–96. http://dx.doi.org/10.1016/j.jco.2007.05.002.

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Müller, Christian, Fabian Weysser, Thomas Mrziglod, and Andreas Schuppert. "Markov-Chain Monte-Carlo methods and non-identifiabilities." Monte Carlo Methods and Applications 24, no. 3 (September 1, 2018): 203–14. http://dx.doi.org/10.1515/mcma-2018-0018.

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Abstract We consider the problem of sampling from high-dimensional likelihood functions with large amounts of non-identifiabilities via Markov-Chain Monte-Carlo algorithms. Non-identifiabilities are problematic for commonly used proposal densities, leading to a low effective sample size. To address this problem, we introduce a regularization method using an artificial prior, which restricts non-identifiable parts of the likelihood function. This enables us to sample the posterior using common MCMC methods more efficiently. We demonstrate this with three MCMC methods on a likelihood based on a complex, high-dimensional blood coagulation model and a single series of measurements. By using the approximation of the artificial prior for the non-identifiable directions, we obtain a sample quality criterion. Unlike other sample quality criteria, it is valid even for short chain lengths. We use the criterion to compare the following three MCMC variants: The Random Walk Metropolis Hastings, the Adaptive Metropolis Hastings and the Metropolis adjusted Langevin algorithm.
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Petrila, Iulian, and Vasile Manta. "Metropolis Monte Carlo analysis of all-optical switching." Computer Physics Communications 185, no. 11 (November 2014): 2874–78. http://dx.doi.org/10.1016/j.cpc.2014.07.008.

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Kamatani, K. "Ergodicity of Markov chain Monte Carlo with reversible proposal." Journal of Applied Probability 54, no. 2 (June 2017): 638–54. http://dx.doi.org/10.1017/jpr.2017.22.

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Abstract We describe the ergodic properties of some Metropolis–Hastings algorithms for heavy-tailed target distributions. The results of these algorithms are usually analyzed under a subgeometric ergodic framework, but we prove that the mixed preconditioned Crank–Nicolson (MpCN) algorithm has geometric ergodicity even for heavy-tailed target distributions. This useful property comes from the fact that, under a suitable transformation, the MpCN algorithm becomes a random-walk Metropolis algorithm.
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SCHULTE, MELANIE, and CAROLINE DROPE. "3D ISING NONUNIVERSALITY: A MONTE CARLO STUDY." International Journal of Modern Physics C 16, no. 08 (August 2005): 1217–24. http://dx.doi.org/10.1142/s0129183105007844.

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We investigate as a member of the Ising universality class the Next-Nearest Neighbor Ising model without external field on a simple cubic lattice by using the Monte Carlo Metropolis Algorithm. The Binder cumulant and the susceptibility ratio, which should be universal quantities at the critical point, were shown to vary for small negative next-nearest neighbor interactions.
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DILGER, H. "TOPOLOGICAL ZERO MODES IN MONTE CARLO SIMULATIONS." International Journal of Modern Physics C 06, no. 01 (February 1995): 123–34. http://dx.doi.org/10.1142/s0129183195000101.

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We present an improvement of global Metropolis updating steps, the instanton hits, used in a hybrid Monte Carlo simulation of the two-flavor Schwinger model with staggered fermions. These hits are designed to change the topological sector of the gauge field. In order to match these hits to an unquenched simulation with pseudofermions, the approximate zero mode structure of the lattice Dirac operator has to be considered explicitly.
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Dissertations / Theses on the topic "Monte Carlo Metropolis"

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Edström, Filip. "Parametrization of Reactive Force Field using Metropolis Monte Carlo." Thesis, Umeå universitet, Institutionen för fysik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-161972.

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Zeppilli, Giulia. "Alcune applicazioni del Metodo Monte Carlo." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/3091/.

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Rönnby, Karl. "Monte Carlo Simulations for Chemical Systems." Thesis, Linköpings universitet, Matematiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-132811.

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This thesis investigates dierent types of Monte Carlo estimators for use in computationof chemical system, mainly to be used in calculating surface growthand evolution of SiC. Monte Carlo methods are a class of algorithms using randomsampling to numerical solve problems and are used in many cases. Threedierent types of Monte Carlo methods are studied, a simple Monte Carlo estimatorand two types of Markov chain Monte Carlo Metropolis algorithm MonteCarlo and kinetic Monte Carlo. The mathematical background is given for allmethods and they are tested both on smaller system, with known results tocheck their mathematical and chemical soundness and on larger surface systemas an example on how they could be used
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Graham, Matthew McKenzie. "Auxiliary variable Markov chain Monte Carlo methods." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/28962.

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Markov chain Monte Carlo (MCMC) methods are a widely applicable class of algorithms for estimating integrals in statistical inference problems. A common approach in MCMC methods is to introduce additional auxiliary variables into the Markov chain state and perform transitions in the joint space of target and auxiliary variables. In this thesis we consider novel methods for using auxiliary variables within MCMC methods to allow approximate inference in otherwise intractable models and to improve sampling performance in models exhibiting challenging properties such as multimodality. We first consider the pseudo-marginal framework. This extends the Metropolis–Hastings algorithm to cases where we only have access to an unbiased estimator of the density of target distribution. The resulting chains can sometimes show ‘sticking’ behaviour where long series of proposed updates are rejected. Further the algorithms can be difficult to tune and it is not immediately clear how to generalise the approach to alternative transition operators. We show that if the auxiliary variables used in the density estimator are included in the chain state it is possible to use new transition operators such as those based on slice-sampling algorithms within a pseudo-marginal setting. This auxiliary pseudo-marginal approach leads to easier to tune methods and is often able to improve sampling efficiency over existing approaches. As a second contribution we consider inference in probabilistic models defined via a generative process with the probability density of the outputs of this process only implicitly defined. The approximate Bayesian computation (ABC) framework allows inference in such models when conditioning on the values of observed model variables by making the approximation that generated observed variables are ‘close’ rather than exactly equal to observed data. Although making the inference problem more tractable, the approximation error introduced in ABC methods can be difficult to quantify and standard algorithms tend to perform poorly when conditioning on high dimensional observations. This often requires further approximation by reducing the observations to lower dimensional summary statistics. We show how including all of the random variables used in generating model outputs as auxiliary variables in a Markov chain state can allow the use of more efficient and robust MCMC methods such as slice sampling and Hamiltonian Monte Carlo (HMC) within an ABC framework. In some cases this can allow inference when conditioning on the full set of observed values when standard ABC methods require reduction to lower dimensional summaries for tractability. Further we introduce a novel constrained HMC method for performing inference in a restricted class of differentiable generative models which allows conditioning the generated observed variables to be arbitrarily close to observed data while maintaining computational tractability. As a final topicwe consider the use of an auxiliary temperature variable in MCMC methods to improve exploration of multimodal target densities and allow estimation of normalising constants. Existing approaches such as simulated tempering and annealed importance sampling use temperature variables which take on only a discrete set of values. The performance of these methods can be sensitive to the number and spacing of the temperature values used, and the discrete nature of the temperature variable prevents the use of gradient-based methods such as HMC to update the temperature alongside the target variables. We introduce new MCMC methods which instead use a continuous temperature variable. This both removes the need to tune the choice of discrete temperature values and allows the temperature variable to be updated jointly with the target variables within a HMC method.
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Paixão, Everton Luiz Martins da. "Estudos de nanoestruturas magnéticas - nanodiscos com impurezas e nanofitas - Via Monte Carlo Metropolis." Universidade Federal de Juiz de Fora (UFJF), 2013. https://repositorio.ufjf.br/jspui/handle/ufjf/4894.

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CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Neste trabalho usamos o método de Monte Carlo Metropolis aplicado em sistemas magnéticos para estudar nanoestruturas como nanodiscos e nanofios de Permalloy. Dividimos em duas partes. Primeiramente, estudou-se o comportamento do núcleo do vórtice rodeado por anéis de impurezas em nanodiscos de permalloy. Variamos o raio e a espessura dos anéis e medimos o limite do campo aplicado para que o núcleo do vórtice passe através destes anéis. Em segundo lugar, temos estudado os estados fundamentais (de menor energia) para nanofios para uma pequena região do espaço de fase. Podemos identificar as configurações de rotação associadas a estes estados.
In this work we have used Monte Carlo Metropolis method applied in magnetic systems to study nanostructures like permalloy nanodisks and nanowires. We divided it in two parts. First, we have studied the behavior of the vortex core surrounded by rings of the impurities in permalloy nanodisks. We have varied the radius and the thickness of the rings and we measure the limit of the applied field for that the vortex core passes through the ring. Second, we have studied the ground states (lowest-energy state) for nanowires to a small region of the phase space. We can identify the spin configurations associated to the these states.
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Ounaissi, Daoud. "Méthodes quasi-Monte Carlo et Monte Carlo : application aux calculs des estimateurs Lasso et Lasso bayésien." Thesis, Lille 1, 2016. http://www.theses.fr/2016LIL10043/document.

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La thèse contient 6 chapitres. Le premier chapitre contient une introduction à la régression linéaire et aux problèmes Lasso et Lasso bayésien. Le chapitre 2 rappelle les algorithmes d’optimisation convexe et présente l’algorithme FISTA pour calculer l’estimateur Lasso. La statistique de la convergence de cet algorithme est aussi donnée dans ce chapitre en utilisant l’entropie et l’estimateur de Pitman-Yor. Le chapitre 3 est consacré à la comparaison des méthodes quasi-Monte Carlo et Monte Carlo dans les calculs numériques du Lasso bayésien. Il sort de cette comparaison que les points de Hammersely donne les meilleurs résultats. Le chapitre 4 donne une interprétation géométrique de la fonction de partition du Lasso bayésien et l’exprime en fonction de la fonction Gamma incomplète. Ceci nous a permis de donner un critère de convergence pour l’algorithme de Metropolis Hastings. Le chapitre 5 présente l’estimateur bayésien comme la loi limite d’une équation différentielle stochastique multivariée. Ceci nous a permis de calculer le Lasso bayésien en utilisant les schémas numériques semi implicite et explicite d’Euler et les méthodes de Monte Carlo, Monte Carlo à plusieurs couches (MLMC) et l’algorithme de Metropolis Hastings. La comparaison des coûts de calcul montre que le couple (schéma semi-implicite d’Euler, MLMC) gagne contre les autres couples (schéma, méthode). Finalement dans le chapitre 6 nous avons trouvé la vitesse de convergence du Lasso bayésien vers le Lasso lorsque le rapport signal/bruit est constant et le bruit tend vers 0. Ceci nous a permis de donner de nouveaux critères pour la convergence de l’algorithme de Metropolis Hastings
The thesis contains 6 chapters. The first chapter contains an introduction to linear regression, the Lasso and the Bayesian Lasso problems. Chapter 2 recalls the convex optimization algorithms and presents the Fista algorithm for calculating the Lasso estimator. The properties of the convergence of this algorithm is also given in this chapter using the entropy estimator and Pitman-Yor estimator. Chapter 3 is devoted to comparison of Monte Carlo and quasi-Monte Carlo methods in numerical calculations of Bayesian Lasso. It comes out of this comparison that the Hammersely points give the best results. Chapter 4 gives a geometric interpretation of the partition function of the Bayesian lasso expressed as a function of the incomplete Gamma function. This allowed us to give a convergence criterion for the Metropolis Hastings algorithm. Chapter 5 presents the Bayesian estimator as the law limit a multivariate stochastic differential equation. This allowed us to calculate the Bayesian Lasso using numerical schemes semi-implicit and explicit Euler and methods of Monte Carlo, Monte Carlo multilevel (MLMC) and Metropolis Hastings algorithm. Comparing the calculation costs shows the couple (semi-implicit Euler scheme, MLMC) wins against the other couples (scheme method). Finally in chapter 6 we found the Lasso convergence rate of the Bayesian Lasso when the signal / noise ratio is constant and when the noise tends to 0. This allowed us to provide a new criteria for the convergence of the Metropolis algorithm Hastings
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Cunha, João Victor de Souza. "Aplicação de Monte Carlo para a geração de ensembles e análise termodinâmica da interação biomolecular." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/76/76132/tde-25112016-143220/.

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As interações moleculares, em especial as de caráter não-covalente, são processos-chave em vários aspectos da biologia celular e molecular, desde a comunicação entre as células ou da velocidade e especificidade das reações enzimáticas. Portanto, há a necessidade de estudar e criar métodos preditivos para calcular a afinidade entre moléculas nos processos de interação, os quais encontram uma gama de aplicações, incluindo a descoberta de novos fármacos. No geral, entre esses valores de afinidade, o mais importante é a energia livre de ligação, que normalmente é determinada por modos computacionalmente rápidos, porém sem uma forte base teórica, ou por cálculos muito complexos, utilizando dinâmica molecular, onde mesmo com um grande poder de determinação da afinidade, é muito custoso computacionalmente. O objetivo deste trabalho é avaliar um modelo menos custoso computacionalmente e que promova um aprofundamento na avaliação de resultados obtidos a partir de simulações de docking molecular. Para esta finalidade, o método de Monte Carlo é empregado para a amostragem de orientações e conformações do ligante do sítio ativo macromolecular. A avaliação desta metodologia demonstrou que é possível calcular grandezas entrópicas e entálpicas e analisar a capacidade interativa entre complexos proteína-ligante de forma satisfatória para o complexo lisozima do bacteriófago T4.
The molecular interactions, especially the ones with a non-covalent nature, are key processes in general aspects of cellular and molecular biology, including cellular communication and velocity and specificity of enzymatic reactions. So, there is a strong need for studies and development of methods for the calculation of the affinity on interaction processes, since these have a wide range of applications like rational drug design. The free energy of binding is the most important measure among the affinity measurements. It can be calculated by quick computational means, but lacking on strong theoretical basis or by complex calculations using molecular dynamics, where one can compute accurate results but at the price of an increased computer power. The aim of this project is to evaluate a computationally inexpensive model which can improve the results from molecular docking simulations. For this end, the Monte Carlo method is implemented to sample different ligand configurations inside the macromolecular binding site. The evaluation of this methodology showed that is possible to calculate entropy and enthalpy, along analyzing the interactive capacity between receptor-ligands complexes in a satisfactory way for the bacteriophage T4.
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Bäckström, Nils, Jonathan Löfgren, and Vilhelm Rydén. "Study of Magnetic Nanostructures using Micromagnetic Simulations and Monte Carlo Methods." Thesis, Uppsala universitet, Materialfysik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-227804.

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We perform micromagnetic simulations in MuMax3 on various magneticnanostructures to study their magnetic state and response to external fields. Theinteraction and ordering of nanomagnetic arrays is investigated by calculating themagnetostatic energies for various configurations. These energies are then used inMonte Carlo simulation to study the thermal behaviour of systems of nanomagneticarrays. We find that the magnetic state of the nanostructures are related to theirshape and size and furthermore affect the emergent properties of the system, givingrise to temperature dependent ordering among the individual structures. Results fromboth micromagnetic and statistical mechanic simulations agree well with availableexperimental data, although the Monte Carlo algorithm encounter problems at lowsimulation temperatures.
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Lima, J?nior Francisco Biagione de. "Simula??es de Monte Carlo para os modelos Ising e Blume-Capel em redes complexa." Universidade Federal do Rio Grande do Norte, 2013. http://repositorio.ufrn.br:8080/jspui/handle/123456789/18606.

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We studied the Ising model ferromagnetic as spin-1/2 and the Blume-Capel model as spin-1, > 0 on small world network, using computer simulation through the Metropolis algorithm. We calculated macroscopic quantities of the system, such as internal energy, magnetization, specific heat, magnetic susceptibility and Binder cumulant. We found for the Ising model the same result obtained by Koreans H. Hong, Beom Jun Kim and M. Y. Choi [6] and critical behavior similar Blume-Capel model
?Neste trabalho estudamos o modelo de Ising ferromagn?tico com spin-1/2 e o modelo Blume-Capel com spin-1, ? > 0 em rede mundo pequeno, usando simula??o computacional atrav?s do algoritmo de Metropolis. Calculamos grandezas macrosc?picas do sistema, tais como a energia interna, a magnetiza??o, o calor espec?fico, a susceptibilidade magn?tica e o cumulante de Binder. Encontramos para o modelo de Ising o mesmo resultado obtido pelos Coreanos H. Hong, Beom Jun Kim e M. Y. Choi [6] e um comportamento cr?tico similar o modelo Blume-Capel.
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Caballero, Nolte Rafael Eduardo. "Monte Carlo - Metropolis Investigations of Shape and Matrix Effects in 2D and 3D Spin-Crossover Nanoparticles." Master's thesis, Pontificia Universidad Católica del Perú, 2017. http://tesis.pucp.edu.pe/repositorio/handle/123456789/8646.

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Se estudia un modelo tipo Ising tomando en cuenta las interacciones de corto y largo alcance, así mismo como el posible efecto de la superficie del sistema y la forma del mismo sobre las propiedades magnéticas del material. Esto se realiza para investigar el comportamiento de los sistemas compuestos por nanopartículas ordenadas en una matriz. Ademas se analiza el papel que juega la relación entre numero de partículas en la superficie con las que se encuentran en el volumen de la matriz con respecto al comportamiento de histeresis del sistema.
An Ising model is studied, taking into account short and long range interactions, as well as the possible effect of the system surface and its shape on the magnetic properties of the material. This is done to investigate the behavior of systems composed of nanoparticles ordered in a matrix. In addition, the role of the relationship between the number of particles on the surface and those in the volume of the matrix with respect to the behavior of system hysteresis is analyzed.
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Books on the topic "Monte Carlo Metropolis"

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Allen, Michael P., and Dominic J. Tildesley. Monte Carlo methods. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803195.003.0004.

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The estimation of integrals by Monte Carlo sampling is introduced through a simple example. The chapter then explains importance sampling, and the use of the Metropolis and Barker forms of the transition matrix defined in terms of the underlying matrix of the Markov chain. The creation of an appropriately weighted set of states in the canonical ensemble is described in detail and the method is extended to the isothermal–isobaric, grand canonical and semi-grand ensembles. The Monte Carlo simulation of molecular fluids and fluids containing flexible molecules using a reptation algorithm is discussed. The parallel tempering or replica exchange method for more efficient exploration of the phase space is introduced, and recent advances including solute tempering and convective replica exchange algorithms are described.
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Coolen, A. C. C., A. Annibale, and E. S. Roberts. Markov Chain Monte Carlo sampling of graphs. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0006.

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This chapter looks at Markov Chain Monte Carlo techniques to generate hard- and soft-constrained exponential random graph ensembles. The essence is to define a Markov chain based on ergodic randomization moves acting on a network with transition probabilities which satisfy detailed balance. This is sufficient to ensure that the Markov chain will sample from the ensemble with the desired probabilities. This chapter studies several commonly seen randomization move sets and carefully defines acceptance probabilities for a range of different ensembles using both the Metropolis–Hastings and the Glauber prescription. Particular care is paid to describe and avoid the pitfalls that can occur in defining randomization moves for hard-constrained ensembles, and applying them without introducing inadvertent bias (i.e. defining and comparing protocols including switch-and-hold and mobility).
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The Monte Carlo Method in the Physical Sciences: Celebrating the 50th Anniversary of the Metropolis Algorithm (AIP Conference Proceedings). American Institute of Physics, 2003.

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The Monte Carlo method in the physical sciences: Celebrating the 50th anniversary of the Metropolis Algorithm : Los Alamos, New Mexico, 9-11 June 2003. Melville, N.Y: American Institute of Physics, 2003.

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Book chapters on the topic "Monte Carlo Metropolis"

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Barbu, Adrian, and Song-Chun Zhu. "Metropolis Methods and Variants." In Monte Carlo Methods, 71–96. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-2971-5_4.

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Juarez-Martinez, Gabriela, Alessandro Chiolerio, Paolo Allia, Martino Poggio, Christian L. Degen, Li Zhang, Bradley J. Nelson, et al. "Metropolis Monte Carlo Method." In Encyclopedia of Nanotechnology, 1369. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-90-481-9751-4_100410.

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Beskos, Alexandros, and Andrew Stuart. "Computational Complexity of Metropolis-Hastings Methods in High Dimensions." In Monte Carlo and Quasi-Monte Carlo Methods 2008, 61–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04107-5_4.

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Robert, Christian P., and George Casella. "Metropolis–Hastings Algorithms." In Introducing Monte Carlo Methods with R, 167–97. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-1576-4_6.

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Robert, Christian P., and George Casella. "Algorithmes de Metropolis-Hastings." In Méthodes de Monte-Carlo avec R, 141–71. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0181-0_6.

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Winkler, Gerhard. "Metropolis Algorithms." In Image Analysis, Random Fields and Dynamic Monte Carlo Methods, 133–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-97522-6_9.

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Winkler, Gerhard. "Metropolis Algorithms." In Image Analysis, Random Fields and Markov Chain Monte Carlo Methods, 179–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55760-6_11.

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Tanner, Martin A. "Markov Chain Monte Carlo: The Gibbs Sampler and the Metropolis Algorithm." In Tools for Statistical Inference, 102–46. New York, NY: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4684-0192-9_6.

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Tanner, Martin A. "Markov Chain Monte Carlo: The Gibbs Sampler and the Metropolis Algorithm." In Tools for Statistical Inference, 137–92. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-4024-2_6.

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"Metropolis-Hastings algorithms." In Markov Chain Monte Carlo, 209–54. Chapman and Hall/CRC, 2006. http://dx.doi.org/10.1201/9781482296426-9.

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Conference papers on the topic "Monte Carlo Metropolis"

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Amini, Abolfazl M. "Metropolis Monte Carlo annealing." In AeroSense 2000, edited by Stephen K. Park and Zia-ur Rahman. SPIE, 2000. http://dx.doi.org/10.1117/12.390481.

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Amini, Abolfazl M. "Metropolis Monte Carlo deconvolution." In AeroSense '99, edited by Stephen K. Park and Richard D. Juday. SPIE, 1999. http://dx.doi.org/10.1117/12.354714.

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Amini, Abolfazl M. "Review of metropolis Monte Carlo image enhancements." In SPIE Defense, Security, and Sensing, edited by Zia-ur Rahman, Stephen E. Reichenbach, and Mark A. Neifeld. SPIE, 2011. http://dx.doi.org/10.1117/12.883986.

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Ceperley, D. M. "Metropolis Methods for Quantum Monte Carlo Simulations." In THE MONTE CARLO METHOD IN THE PHYSICAL SCIENCES: Celebrating the 50th Anniversary of the Metropolis Algorithm. AIP, 2003. http://dx.doi.org/10.1063/1.1632120.

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Landau, David P. "The Metropolis Monte Carlo Method in Statistical Physics." In THE MONTE CARLO METHOD IN THE PHYSICAL SCIENCES: Celebrating the 50th Anniversary of the Metropolis Algorithm. AIP, 2003. http://dx.doi.org/10.1063/1.1632124.

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Wilding, Nigel B. "Phase Switch Monte Carlo." In THE MONTE CARLO METHOD IN THE PHYSICAL SCIENCES: Celebrating the 50th Anniversary of the Metropolis Algorithm. AIP, 2003. http://dx.doi.org/10.1063/1.1632147.

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Frenkel, D. "Biased Monte Carlo Methods." In THE MONTE CARLO METHOD IN THE PHYSICAL SCIENCES: Celebrating the 50th Anniversary of the Metropolis Algorithm. AIP, 2003. http://dx.doi.org/10.1063/1.1632121.

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Okamoto, Yuko. "Metropolis Algorithms in Generalized Ensemble." In THE MONTE CARLO METHOD IN THE PHYSICAL SCIENCES: Celebrating the 50th Anniversary of the Metropolis Algorithm. AIP, 2003. http://dx.doi.org/10.1063/1.1632136.

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Dewing, Mark. "Metropolis with noise: The penalty method." In THE MONTE CARLO METHOD IN THE PHYSICAL SCIENCES: Celebrating the 50th Anniversary of the Metropolis Algorithm. AIP, 2003. http://dx.doi.org/10.1063/1.1632154.

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Novotny, M. A. "Algorithms for Faster and Larger Dynamic Metropolis Simulations." In THE MONTE CARLO METHOD IN THE PHYSICAL SCIENCES: Celebrating the 50th Anniversary of the Metropolis Algorithm. AIP, 2003. http://dx.doi.org/10.1063/1.1632135.

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Reports on the topic "Monte Carlo Metropolis"

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Bates, Cameron Russell, and Edward Allen Mckigney. Metis: A Pure Metropolis Markov Chain Monte Carlo Bayesian Inference Library. Office of Scientific and Technical Information (OSTI), January 2018. http://dx.doi.org/10.2172/1417145.

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