Fully Gibbs Sampling Algorithms for Bayesian Variable Selection in Latent Regression Models

This study proposed efficient Gibbs sampling algorithms for variable selection in a latent regression model under a unidimensional two-parameter logistic item response theory model. Three types of shrinkage priors were employed to obtain shrinkage estimates: double-exponential (i.e., Laplace), horseshoe, and horseshoe+ priors. These shrinkage priors were compared to a uniform prior case in both simulation and real data analysis. The simulation study revealed that two types of horseshoe priors had a smaller root mean square errors and shorter 95% credible interval lengths than double-exponential or uniform priors. In addition, the horseshoe prior+ was slightly more stable than the horseshoe prior. The real data example successfully proved the utility of horseshoe and horseshoe+ priors in selecting effective predictive covariates for math achievement. In the final section, we discuss the benefits and limitations of the three types of Bayesian variable selection methods.

NMR-Based Configurational Assignments of Natural Products: Gibbs Sampling and Bayesian Inference Using Floating Chirality Distance Geometry Calculations

Floating chirality restrained distance geometry (fc-rDG) calculations are used to directly evolve structures from NMR data such as NOE-derived intramolecular distances or anisotropic residual dipolar couplings (RDCs). In contrast to evaluating pre-calculated structures against NMR restraints, multiple configurations (diastereomers) and conformations are generated automatically within the experimental limits. In this report, we show that the “unphysical” rDG pseudo energies defined from NMR violations bear statistical significance, which allows assigning probabilities to configurational assignments made that are fully compatible with the method of Bayesian inference. These “diastereomeric differentiabilities” then even become almost independent of the actual values of the force constants used to model the restraints originating from NOE or RDC data.

COMMTRUST

We propose CommTrust for trust evaluation by mining feedback comments. Our main contributions include: 1) we propose a multidimensional trust model for computing reputation scores from user feedback comments; and 2) we propose an algorithm for mining feedback comments for dimension ratings and weights, combining techniques of natural language processing, opinion mining, and topic modeling. Extensive experiments on eBay and Amazon data demonstrate that CommTrust can effectively address the “all good reputation” issue and rank sellers effectively. To the best of our knowledge, our research is the first piece of work on trust evaluation by mining feedback comments.. An algorithm is proposed to mine feedback comments for dimension weights, ratings, which combine methods of topic modeling, natural language processing and opinion mining. This model has been experimenting with the dataset which includes various user level feedback comments that are obtained on various products. It also finds various multi-dimensional features and their ratings using Gibbs-sampling that generates various categories for feedback and assigns trust score for each dimension under each product level. Keywords: E-Commerce, Feedback mining, Trust score, Topic modeling, Reputation-based trust score

Comparison of Bayesian Methods for Recovering Sinusoids

In this paper, we study a problem of estimating parameters of sinusoids from noisy data within Bayesian inferential framework. In this context, three different computational schemes such as, Bretthorst’s integral method (BRETTHORST), Gibbs sampling (GIBBS) and parallel tempering (PT) are studied and modifications of their algorithms were tested on data generated from synthetic signals. In addition, our emphasis is given to a comparison of their performances with respect to Cramér-Rao lower bound (CRLB).

Performance Analysis of Gibbs Sampling for Bayesian Extracting Sinusoids

This paper involves problems of estimating parameters of sinusoids from white noisy data by using Gibbs sampling (GS) in a Bayesian framework. Modifications of its algorithm is tested on data generated from synthetic signals and its performance is compared with conventional estimators such as Maximum Likelihood(ML) and Discrete Fourier Transform (DFT) under a variety of signal to noise ratio (SNR) and different length of data sampling (N), regarding to Cramér-Rao lower bound (CRLB). All simulation results show its effectiveness in frequency and amplitude estimation of sinusoids.

PENERAPAN METODE REGRESI KUANTIL BAYESIAN PADA PEMODELAN LAMA RAWAT INAP PASIEN COVID-19

Penelitian ini bertujuan untuk mengetahui faktor-faktor yang mempengaruhi lama rawat inap pasien COVID-19. Dalam hal ini variabel tak bebas yang digunakan adalah data lama rawat pasien COVID-19 dengan variabel bebasnya adalah Usia, Jenis Kelamin, Diagnosa Pasien COVID-19, dan Komorbid. Data lama rawat pasien COVID-19 tidak memenuhi asumsi kenormalan sehingga diatasi dengan pendekatan pendugaan parameter menggunakan metode regresi kuantil Bayesian. Adapun pada metode ini pendugaan parameter diestimasi dengan mengasumsikan bahwa error data berdistribusi Asymmetric Laplace, yang kemudian dibentuk sebagai fungsi likelihood-nya. Pendekatan Bayesian pada regresi kuantil menggunakan MCMC dengan algoritma Gibbs sampling untuk menghasilkan mean posterior. Indikator ketepatan model diperoleh dari perhitungan nilai pseudo R2 tertinggi. Penelitian ini diperoleh kuantil 0,75 sebagai kuantil terbaik dengan variabel Komorbid sebagai variabel yang berpengaruh signifikan dalam mempengaruhi lama rawat inap pasien COVID-19.Kata Kunci: COVID-19, Regresi Kuantil, Bayesian

Mitigating the noisy solution impact of mixed Gibbs sampling detector in high-order modulation large-scale MIMO systems

A neighborhood-restricted mixed Gibbs sampling (MGS)-based approach is proposed for low-complexity high-order modulation large-scale multiple-input multiple-output (LS-MIMO) detection. The proposed LS-MIMO detector applies a neighborhood limitation (NL) on the noisy solution from the MGS at a distance d — thus, named d-simplified MGS (d-sMGS) — in order to mitigate its impact, which can be harmful when a high-order modulation is considered. Numerical simulation results considering 64-QAM demonstrated that the proposed detection method can substantially improve the MGS algorithm convergence, whereas no extra computational complexity per iteration is required. The proposed d-sMGS-based detector suitable for high-order modulation LS-MIMO further exhibits improved performance × complexity tradeoff when the system loading is high, i.e., when $\frac {K}{N}\geq 0.75$ K N ≥ 0.75 . Also, with increasing the number of dimensions, i.e., increasing number of antennas and/or modulation order, a smaller restriction of 2-sMGS was shown to be a more interesting choice than 1-sMGS.