[PDF] Top 20 Stochastic gradient Markov chain Monte Carlo
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Stochastic gradient Markov chain Monte Carlo
... for Monte Carlo sampling, which is known as the unadjusted Langevin ...the gradient of the log-posterior density requires the evaluation of the full ...by stochastic gradient descent ... See full document
31
Large scale Bayesian computation using Stochastic Gradient Markov Chain Monte Carlo
... nary differential equation, rather than a stochastic differential equation (see e.g. Neal, 2010); and it can be shown that these two versions are related (see e.g. Horowitz, 1991; Leimkuhler and Shang, 2016). This ... See full document
221
Bayesian Inference for PCFGs via Markov Chain Monte Carlo
... two Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference of probabilistic con- text free grammars (PCFGs) from ter- minal strings, providing an alternative to maximum-likelihood ... See full document
8
Designing An Efficient Real Time Summon Acuity System For Physically Drained Human
... Hidden Markov Model (HMM) is presented for gesture trajectory modeling and ...data. Markov Chain Monte Carlo (MCMC) plays a positive role in Bayesian statistical ... See full document
7
Bayesian System Identification of Nonlinear Dynamical Systems using a Fast MCMC Algorithm
... of Markov chain Monte Carlo (MCMC) ...ergodic Markov chain whose stationary distri- bution is equal to P ( θ |D, M) such that, once the chain has converged, it can be used ... See full document
7
On Perturbed Proximal Gradient Algorithms
... proximal gradient algorithm for which the gradient is intractable and is approximated by Monte Carlo methods (and in particular Markov Chain Monte ...the Monte ... See full document
33
Information geometric Markov chain Monte Carlo methods using diffusions
... on Markov chains that explore the space locally, like the RWM and MALA, it may be advantageous to instead impose a different metric structure on the space, X , so that some points are drawn closer together and ... See full document
30
CAS: Dependencies in Stochastic Loss Reserve Models
... Bayesian Markov chain Monte Carlo (MCMC) sto chastic loss reserve model for two separate lines of insurance, this paper describes how to fit a bivariate stochastic model that captures ... See full document
21
Stability of sequential Markov Chain Monte Carlo methods
... time-homogeneous Markov processes (see ...of Markov Chain Monte Carlo (MCMC) methods based on reversible Markov chains (see ...ergodic Markov chain having µ as ... See full document
10
Markov chain Monte Carlo analysis of cholera epidemic
... As a future work, the model will be validated by using real data of cholera cases collected from Singida, Dodoma and Dar es salaam regions in Tanzania. The mathematical model devel- oped in this study will be extended to ... See full document
27
Uncovering mental representations with Markov chain Monte Carlo
... Discriminative judgments can be predicted using the probability distributions esti- mated by MCMC, by making assumptions about the decision process that participants use. The discrimination predictions based on MCMC ... See full document
57
Speculative moves : multithreading Markov Chain Monte Carlo programs
... Although by definition a Markov chain consists of a strictly sequential series of state changes, each MCMC iteration will not necessary result in a state change. In each iteration (see figure 2) a state ... See full document
13
On the containment condition for adaptive Markov Chain Monte Carlo algorithms
... decreasing, and at least exponentially tailed. However, for adaptive Metropolis-within-Gibbs algo- rithms, the target density is only required to be exponentially tailed on the direction of coordinates, and strong ... See full document
26
MODELLING OF STOCK PRICES BY THE MARKOV CHAIN MONTE CARLO METHOD
... Eimutis Valakevičius – Doctor, Associated professor. Faculty of Fundamental Sciences, Kaunas University of Technology. Diploma Applied mathematics, VU (1978), PhD (1989), Associated Professor (1991), Head of Department ... See full document
13
Accelerating Markov chain Monte Carlo via parallel predictive prefetching
... of these computations. The master maintains data structures that organize the results of potentially useful increments of computational work, plus related information. These incre- ments of work include all those that ... See full document
128
Probabilistic Prognostics and Health Management for Fatigue-critical Components using High-fidelity Models.
... The last two chapters have presented the multi-decade progression of the field of PHM. Born out of reliability analysis and the concept of condition-based maintenance, the PHM methodology – that is, the use of in-situ, ... See full document
189
Stochastic simulation and spatial statistics of large datasets using parallel computing
... and Markov Chain Monte Carlo (MCMC) methods are discussed from a parallel computing perspective as ...Single chain MCMC methods are also examined and improved upon to give faster ... See full document
153
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- ... See full document
148
Principled Selection of Hyperparameters in the Latent Dirichlet Allocation Model
... Latent Dirichlet Allocation (LDA) is a well known topic model that is often used to make inference regarding the properties of collections of text documents. LDA is a hierarchical Bayesian model, and involves a prior ... See full document
38
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] ... See full document
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