We present a method for controlling the output of procedural modeling programs using Sequential Monte Carlo (SMC). Previous probabilistic methods for controlling procedural models use Markov Chain Monte Carlo (MCMC), which receives control feedback only for completely-generated models. In contrast, SMC receives feedback incrementally on incomplete models, allowing it to reallocate computational...

As the world advances, statisticians/mathematicians are being involved into more and more complex surveys for the development of society and human beings. Consequently, these complex survey data requires complicated and high-dimensional models for final analysis. We need sophisticated and efficient statistical/mathematical tools for estimation and prediction of these models. Frequently, we simu...

The MCMC (Markov Chain Monte-Carlo) method [1] has played an important role in study of complex systems with many degrees of freedom. For example, MCMC has been applied to various many-body problems such as proteins [2], spin systems [3], and lattice gauge theory [4]. Although the method has achieved great success, there are systems where Monte-Carlo sampling does not work due to local minima o...

We formulate both Markov chain Monte Carlo (MCMC) sampling algorithms and basic statistical physics in terms of elementary symmetries. This perspective on yields derivations well-known MCMC a new parallel algorithm that appears to converge more quickly than current state the art methods. The symmetry also parsimonious framework for practical approach constructing meaningful notions effective te...

This paper presents a new glottal inverse filtering (GIF) method that utilizes Markov chain Monte Carlo (MCMC) algorithm. First, initial estimates of the vocal tract and glottal flow are evaluated by an existing GIF method, the iterative adaptive inverse filtering (IAIF). Simultaneously, the initially estimated glottal flow is synthesized using the Klatt model and filtered with the estimated vo...

In this work, we demonstrate that applying deep generative machine learning models for lattice field theory is a promising route solving problems where Markov Chain Monte Carlo (MCMC) methods are problematic. More specifically, show can be used to estimate the absolute value of free energy, which in contrast existing MCMC-based limited only energy differences. We effectiveness proposed method t...

The Unobserved ARCH model is a good description of the phenomenon of changing volatility that is commonly appeared in the financial time series. We study this model adopting Bayesian inference via Markov Chain Monte Carlo (MCMC). In order to provide an easy to implement MCMC algorithm we adopt some suitable non-linear transformations of the parameter space such that the resulting MCMC algorithm...

The previous lecture showed that, for self-reducible problems, the problem of estimating the size of the set of feasible solutions is equivalent to the problem of sampling nearly uniformly from that set. This lecture explores the applications of that result by developing techniques for sampling from a uniform distribution. Specifically, this lecture introduces the concept of Markov Chain Monte ...

This article introduces a Markov chain Monte Carlo (MCMC) method for sampling the parameters of a multinomial logit model from their posterior distribution. Let yi ∈ {0, . . . ,M} denote the categorical response of subject i with covariates xi = (xi1, . . . , xip) T . Let X = (x1, . . . ,xn) T denote the design matrix, and let y = (y1, . . . , yn) T . Multinomial logit models relate yi to xi th...

We review three algorithms for Latent Dirichlet Allocation (LDA). Two of them are variational inference algorithms: Variational Bayesian inference and Online Variational Bayesian inference and one is Markov Chain Monte Carlo (MCMC) algorithm – Collapsed Gibbs sampling. We compare their time complexity and performance. We find that online variational Bayesian inference is the fastest algorithm a...