Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 37 for “"Monte Carlo algorithms"”.
-
Evaluating The Efficiency of Markov Chain Monte Carlo Algorithms
<p>Markov chain Monte Carlo (MCMC) is a simulation technique that produces a Markov chain designed to converge to a stationary distribution. In Bayesian statistics, MCMC is used to obtain samples from a posterior distribution for inference. To ensure the accuracy of estimates using MCMC samples, …
-
Ground state and dynamical properties of many-body systems by non conventional Quantum Monte Carlo algorithms
In this work we develop Quantum Monte Carlo techniques suitable for exploring both ground state and dynamical properties of interacting many-body systems. We then apply these techniques to the study of excitations in superfluid He4 and to explore the structure of nuclear systems using chiral …
-
Sequential Modelling and Inference of High-frequency Limit Order Book with State-space Models and Monte Carlo Algorithms
… as well as a number of Bayesian inference algorithms such as particle filtering, Markov chain Monte Carlo and variational inference algorithms, this thesis presents my extensive research into the high-frequency limit order book covering a wide scope of topics. Chapter 2 presents a novel …
-
On a Selection of Advanced Markov Chain Monte Carlo Algorithms for Everyday Use: Weighted Particle Tempering, Practical Reversible Jump, and Extensions
… discovery scales exponentially. Markov chain Monte Carlo (MCMC) is a broad class of powerful algorithms, typically used for Bayesian inference. Despite their variety and versatility, these algorithms rarely become mainstream workhorses because they can be difficult to implement. The humble …
-
Aspects of population Markov chain Monte Carlo and reversible jump Markov chain Monte Carlo
… ideas of two new population Markov chain Monte Carlo algorithms and an automatic proposal mechanism for the Reversible jump Markov chain Monte Carlo algorithm.
-
Ergodicity of Adaptive MCMC and its Applications
Markov chain Monte Carlo algorithms (MCMC) and Adaptive Markov chain Monte Carlo algorithms (AMCMC) are most important methods of approximately sampling from complicated probability distributions and are widely used in statistics, computer science, chemistry, physics, etc. The core problem to use …
-
Eliminating critical slowing down in Monte Carlo calculations
… theory. Traditional Metropolis and heat bath Monte Carlo methods in lattice calculations break down whenever one tries to calculate thermodynamic quantities near critical points; this phenomenon is called Critical Slowing Down, (CSD). Recently, alternate methods have been proposed to shorten …
-
Entropy functions and rare events in disordered systems by transfer matrix calculations and Monte Carlo sampling
… thesis treats problems from bioinformatics with Monte Carlo methods from statistical physics. Methods to compare molecular sequences (sequence alignment) make use of statistical tests to assess the significance of observed similarities. Distributions of optimal alignment scores over random …
-
Nonparametric Bayesian Quantile Regression via Dirichlet Process Mixture Models
… model isstudied carefully. And Markov chain Monte Carlo algorithms are provided for posteriorinference. The performance of our approaches is evaluated using simulated data and realdata. Moreover, we are able to incorporate random effects into our models such that ourapproaches can be extended …
-
Utilizing Hierarchical Clusters in the Design of Effective and Efficient Parallel Simulations of 2-D and 3-D Ising Spin Models
In this work, we design parallel Monte Carlo algorithms for the Ising spin model on a hierarchical cluster. A hierarchical cluster can be considered as a cluster of homogeneous nodes which are partitioned into multiple supernodes such that communication across homogenous clusters is represented by …
-
Novel Quantum Monte Carlo Approaches for Quantum Liquids
Quantum Monte Carlo methods are a powerful suite of techniques for solving the quantum many-body problem. By using random numbers to stochastically sample quantum properties, QMC methods are capable of studying low-temperature quantum systems well beyond the reach of conventional deterministic …
-
Uncertainty and sensitivity analysis for long-running computer codes : a critical review
… analysis (UA) methods reviewed include standard Monte Carlo simulation, Latin Hypercube sampling, importance sampling, line sampling, and subset simulation. Sensitivity analysis (SA) methods include scatter plots, Monte Carlo filtering, regression analysis, variance-based methods (Sobol' …
-
Monte Carlo Methods in Practice and Efficiency Enhancements via Parallel Computation
Monte Carlo methods are crucial when dealing with advanced problems in Bayesian inference. Indeed, common approaches such as Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) can be endlessly adapted to tackle the most complex problems. What is important then is to construct …
-
Sampling in human cognition
… across a wide range of cognition demonstrate Monte-Carlo-like behavior by human observers; moreover, models of cognition based on specific Monte Carlo algorithms can describe previously elusive cognitive phenomena such as perceptual bistability and probability matching. When sampling …
-
Bayesian quantile linear regression
… developed with asymptotic theories and efficient algorithms. However not much work has been done under the Bayesian framework. The most challenging problem for Bayesian quantile regression is that the likelihood is usually not available unless a certain distribution for the error is assumed. In …
-
Max-Stable Processes, Measure Transport & Conditional Sampling
… the two. First, we develop new Markov chain Monte Carlo algorithms for conditional sampling of max-stable processes. Next, we create models that incorporate physical laws, encoded by partial differential equations, to extend max-stable processes into regions without observations. Third, we …
-
Linkage Based Dirichlet Processes
… desire for developing feasible Markov Chain Monte Carlo (MCMC) algorithms for large parameter spaces. Dirichlet Process Mixture Models (DPMMs) have become a Bayesian mainstay for modeling heterogeneous structures, namely clusters, especially when the quantity of clusters is not known with the …
-
Qualitative and quantitative convergence results for randomised integration methods
… methods based on either, randomised Quasi-Monte Carlo, or (adaptive) Markov chain Monte Carlo methods are studied. Depending on the underlying integration problem we show qualitative and quantitative results, which ensure the asymptotic correctness of an algorithm or provide explicit error …
-
Development of Monte-Carlo simulations for III-V semiconductors employing an analytic band-structure
… electron transport in these materials. Ensemble Monte-Carlo algorithms are developed in order to determine the electron transport properties of these materials, coupled with derived expressions for a novel band-structure approximation based on the cosine form that incorporates the inflection …
Page 1 of 2