(This post is by Charles) Art Owen (Stanford) read our paper on nesting Rhat to assess convergence in the many-short-chains regime of MCMC. He made a lot of great comments and asked some clarification questions. Notably: It wasn’t clear to me … Continue reading →

Selected performances from nine of Colonial Williamsburg's recordings, including fife and drum marches, chamber music, slave chants, and tavern songs.

The present invention is a serial to parallel data conversion method and device where new serial data are stored within a first n-bit register prior to presentation at an n-bit parallel output. Subsequently, additional data are stored within a second n-bit register while the data stored within the first register are presented at the parallel output. Data storage and data presentation are thereafter alternated, thereby eliminating the problem of setup time seen in prior art.

An automated method for analyzing seeds generally includes collecting image data from individual seeds using a seed sampling system, determining at least one characteristic of each of the individual seeds based on the collected image data, and removing tissue from each of the individual seeds using the seed sampling system. The method also includes, prior to removing the tissue sample from each of the individual seeds, adjusting at least one operational parameter of the seed sampling system based on the at least one characteristic of the seed from which the tissue is to be removed to thereby allow for generally consistent removal of tissue from each of the individual seeds. In some aspects, the method further includes analyzing the tissue removed from the seeds for presence or absence of at least one characteristic, and selecting seeds based on presence or absence of the at least one characteristic.

An automated seed sampler system includes an imaging device for obtaining images of seeds, an orienting device for orienting the seeds based on the images, and a sampling station for removing tissue from the oriented seeds. In some aspects, the system also includes a transport subsystem for supporting the oriented seeds and conveying the oriented seeds to the sampling station. A method for removing tissue from seeds includes imaging the seeds, orienting the seeds based on image information obtained from the seeds, and removing tissue from the oriented seeds. In some aspects, the method also includes transporting the oriented seeds in a transport subsystem to a sampling station for removing the tissue from the oriented seeds, and/or collecting the tissue removed from the oriented seeds so that a one-to-one correspondence exists between the tissue and the sampled seeds, and/or analyzing the tissue for characteristics indicative of genetic and/or chemical traits.

discoDSP has announced an update to its Bliss performance sampler with built­-in VST host and VSTi sampling. Changes in Bliss v1.8 Added Export Program to mapped WAV files. Added Linux JACK support for standalone. Added Windows ASIO support for standalone. Auto glide mode enabled by default. Delay default value SYNC SYNC 3 ticks. Delay FX […]

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Plugin Boutique has launched an exclusive sale on the Palindrome granular morph plotting sampler by Glitchmachines, offering a 75% discount for the next few days. The basic concept behind Palindrome is to fuse four granular samplers with a coordinate plotting grid and complex modulation sources in order to facilitate the creation of morphing sound effects […]

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Loopmasters has released the 5Pin Media Label Sampler Volume 8, a new compilation that gives you the opportunity to experience a varied selection of material from 5Pin Media’s most recent releases. With ten years experience as a label, we continue to strive to bring producers the best combination of Loops, One-Shots, Presets, Patches and MIDI, […]

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Marco A. R. Ferreira.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 4, 734--744.

Abstract:
We study the limiting behavior of the one-at-a-time Gibbs sampler for the intrinsic conditional autoregressive model with centering on the fly. The intrinsic conditional autoregressive model is widely used as a prior for random effects in hierarchical models for spatial modeling. This model is defined by full conditional distributions that imply an improper joint “density” with a multivariate Gaussian kernel and a singular precision matrix. To guarantee propriety of the posterior distribution, usually at the end of each iteration of the Gibbs sampler the random effects are centered to sum to zero in what is widely known as centering on the fly. While this works well in practice, this informal computational way to recenter the random effects obscures their implied prior distribution and prevents the development of formal Bayesian procedures. Here we show that the implied prior distribution, that is, the limiting distribution of the one-at-a-time Gibbs sampler for the intrinsic conditional autoregressive model with centering on the fly is a singular Gaussian distribution with a covariance matrix that is the Moore–Penrose inverse of the precision matrix. This result has important implications for the development of formal Bayesian procedures such as reference priors and Bayes-factor-based model selection for spatial models.

Adaptive importance samplers are adaptive Monte Carlo algorithms to estimate expectations with respect to some target distribution which extit{adapt} themselves to obtain better estimators over a sequence of iterations. Although it is straightforward to show that they have the same $mathcal{O}(1/sqrt{N})$ convergence rate as standard importance samplers, where $N$ is the number of Monte Carlo samples, the behaviour of adaptive importance samplers over the number of iterations has been left relatively unexplored. In this work, we investigate an adaptation strategy based on convex optimisation which leads to a class of adaptive importance samplers termed extit{optimised adaptive importance samplers} (OAIS). These samplers rely on the iterative minimisation of the $chi^2$-divergence between an exponential-family proposal and the target. The analysed algorithms are closely related to the class of adaptive importance samplers which minimise the variance of the weight function. We first prove non-asymptotic error bounds for the mean squared errors (MSEs) of these algorithms, which explicitly depend on the number of iterations and the number of samples together. The non-asymptotic bounds derived in this paper imply that when the target belongs to the exponential family, the $L_2$ errors of the optimised samplers converge to the optimal rate of $mathcal{O}(1/sqrt{N})$ and the rate of convergence in the number of iterations are explicitly provided. When the target does not belong to the exponential family, the rate of convergence is the same but the asymptotic $L_2$ error increases by a factor $sqrt{ ho^star} > 1$, where $ ho^star - 1$ is the minimum $chi^2$-divergence between the target and an exponential-family proposal.

Ioannis Ntzoufras, Claudia Tarantola, Monia Lupparelli.

Source: Bayesian Analysis, Volume 14, Number 3, 797--823.

Abstract:
We introduce a novel Bayesian approach for quantitative learning for graphical log-linear marginal models. These models belong to curved exponential families that are difficult to handle from a Bayesian perspective. The likelihood cannot be analytically expressed as a function of the marginal log-linear interactions, but only in terms of cell counts or probabilities. Posterior distributions cannot be directly obtained, and Markov Chain Monte Carlo (MCMC) methods are needed. Finally, a well-defined model requires parameter values that lead to compatible marginal probabilities. Hence, any MCMC should account for this important restriction. We construct a fully automatic and efficient MCMC strategy for quantitative learning for such models that handles these problems. While the prior is expressed in terms of the marginal log-linear interactions, we build an MCMC algorithm that employs a proposal on the probability parameter space. The corresponding proposal on the marginal log-linear interactions is obtained via parameter transformation. We exploit a conditional conjugate setup to build an efficient proposal on probability parameters. The proposed methodology is illustrated by a simulation study and a real dataset.

L. F. South, A. N. Pettitt, C. C. Drovandi.

Source: Bayesian Analysis, Volume 14, Number 3, 773--796.

Abstract:
Sequential Monte Carlo (SMC) methods for sampling from the posterior of static Bayesian models are flexible, parallelisable and capable of handling complex targets. However, it is common practice to adopt a Markov chain Monte Carlo (MCMC) kernel with a multivariate normal random walk (RW) proposal in the move step, which can be both inefficient and detrimental for exploring challenging posterior distributions. We develop new SMC methods with independent proposals which allow recycling of all candidates generated in the SMC process and are embarrassingly parallelisable. A novel evidence estimator that is easily computed from the output of our independent SMC is proposed. Our independent proposals are constructed via flexible copula-type models calibrated with the population of SMC particles. We demonstrate through several examples that more precise estimates of posterior expectations and the marginal likelihood can be obtained using fewer likelihood evaluations than the more standard RW approach.

Selected performances from nine of Colonial Williamsburg's recordings, including fife and drum marches, chamber music, slave chants, and tavern songs.