Objective Bayes Covariate-Adjusted Sparse Graphical Model Selection

We present an objective Bayes method for covariance selection in Gaussian multivariate regression models having a sparse regression and covariance structure, the latter being Markov with respect to a Directed Acyclic Graph (DAG). Our procedure can be easily complemented with a variable selection step, so that variable and graphical model selection can be performed jointly. In this way, we oer a solution to a problem of growing importance especially in the area of genetical genomics (eQTL analysis). The input of our method is a single default prior, essentially involving no subjective elicitation, while its output is a closed form marginal likelihood for every covariateadjusted DAG model, which is constant over each class of Markov equivalent DAGs; our procedure thus naturally encompasses covariate-adjusted decomposable graphical models. In realistic experimental studies our method is highly competitive, especially when the number of responses is large relative to the sample size

Learning Markov Equivalence Classes of Directed Acyclic Graphs: an Objective Bayes Approach

A Markov equivalence class contains all the Directed Acyclic Graphs (DAGs) encoding the same conditional independencies, and is represented by a Completed Partially Directed Acyclic Graph (CPDAG), also named Essential Graph (EG).We approach the problem of model selection among noncausal sparse Gaussian DAGs by directly scoring EGs, using an objective Bayes method. Specifically, we construct objective priors for model selection based on the Fractional Bayes Factor, leading to a closed form expression for the marginal likelihood of an EG. Next we propose an MCMC strategy to explore the space of EGs using sparsity constraints, and illustrate the performance of our method on simulation studies, as well as on a real dataset. Our method provides a coherent quantication of inferential uncertainty, requires minimal prior specication, and shows to be competitive in learning the structure of the data-generating EG when compared to alternative state-of-the-art algorithms

TRADISI RUWATAN SANTRI DI BEDINGIN KELURAHAN TIRTOMOYO KECAMATAN TIRTOMOYO KABUPATEN WONOGIRI

Dalam penulisan sk.Iipsi in1 penulis berangkat dari budaya masyarakat yang masih ada dan terns berkembang hingga saat ini terutama masalah upacara (ritus).Upacara merupakan suatu tindakan atau aktifitas manusia dalam, melaksanakan kebaktian terhadap Tuhan, Dewa·Dewa, roh nenek moyang atau makhluk halus Iainnya dan dalam usahanya untuk berkomunikasi dengan Tuhan dan penghuni alam gaib. Di antara upacara- upacara yang masih ada sampai saat ini adalah upacara ruwatan. Ruwatan ini merupakan suatu tradisis upacara yang dilaksanakan oleh sebagian anggota masyarakat (Jawa dan Bali) pada umumnya, tujuannya W1tuk menghilangkan suatu keburukan, kemalangan, noda dan lain-lain yang ada dalanl. diri dan keluarganya, atau yang sering disebut dengan istilah upacara tolak bola maksudnya meminta perlindungan keamanan dan keselamatan dari Tuhan atau Dewa agar terhindar segala marabahaya. Dalam pelaksanaan ruwatan itu sendiri digolongkan menjadi tiga, yaitu: pelaksanaan ruwatan hanya dengan selamatan saja (rasuian),' ru mlan yang disertai dengan pementasan wayang beber dan ruwatan dengan pementasan wayang kulit atau wayang gedog. Narnun demikian ada lagi cara melaksanakan upacara ruwatan yang lain dari ketiga cara tersebut di atas, upacara ruwatan itu adalah ruwatan santri seperti yang dilakukan oleh sebagian warga Dukuh Bedingin Tirtomoyo dan sekitamya . Sebagai suatu tradisi yang masih ada hingga saat ini sudah tentu ada faktor pendukungnya . Begitu pula upacara ruwatan santri yang dilakukan oleh warga masyarakat Bedingin yang terkenal sebagai masyarakat agamis, karena penduduknya mayoritas dan bahkan bisa dikatakan seluruhnya beragama Islam. Namun demikian mereka masih melaksanakan upacara ruwatan santri tersebut dengan adanya anggapan bahwa upacara ruwatan santri ini merupakan kegiatan keagamaan. Mereka beranggapan kegiatan keagamaan karena dalam pelaksanaannya dengan membaca ayat-ayat Al-qur'an, dan itu merupakan ke!:,.Jatan yang tidak bertentangan dengan ajaran agama Islam, karena upacara ruwatan santri merupakan salah satu cara berdo'a kepada Allah swt, dan Do'a itu sendiri merupakan suatu ibadal1. Untuk itulah kemudian penulis berusaha menelaah dan memberikan data seobyektif mungkin mengenai upacara ruwatan santri tersebut. Penelitian ini adalah penelitian lapangan dan dikategorikan jenis penelitian kualitatif dengan pendekatan antropologis sebagai pisau analisa. Dalam penelitia ini penulis mengumpulkan data melalui observasi, interview dan dokumentasi. Berangkat dari sinilah kemudain penulis menyusun sebual1 laporan akhir dalam bentuk skripsi

Introduction to “On the use of Markov chain Monte Carlo methods for the sampling of mixture models” by R. Douc, F. Maire, J. Olsson

We introduce the paper, highlighting contributions and potential fields of applicatio

Parametric and nonparametric Bayesian methods in finance

The objective of the present thesis is to solve some relevant financial problems through applications and generalizations of parametric and nonparametric Bayesian statistical methodologie

Robust Bayesian Graphical Modeling Using Dirichlet t-Distributions. Contributed Discussion

The generalization of the classical multivariate t-distribution to the Dirichlet t-distribution proposed in the paper under discussion allows to model graphs that account for outliers, still keeping a reasonably low computational burden. In this comment we focus on a possible further generalization aiming at incorporating skewness in the analysis

Bayesian Nonparametric State Space Models via Mixture Process of Products of Dirichlet Processes

Parametrically specified measurement and transition equations in State Space Models (SSM) are a source of bias in case of a mismatch between parametric assumptions and reality. The mixture process of products of Dirichlet processes (MPDP) is proposed as a flexible modeling framework for SSMs when there is uncertainty on the distributional assumption in the measurement equation. It is shown that the MPDP prior can approximate any prior belief and that the true parametric SSM can be approximated arbitrarily well by a nonparametric SSM with MPDP prior on the conditional distribution of the observations. An efficient estimation algorithm is designed for posterior sampling, with minimum additional computational effort relative to parametric models. Two simulated exercises on Gaussian Kalman Filtering and Hidden Markov Models, and an empirical application for regime shifts in interest rates, show the better performance of the proposed approach when compared to parametric SSMs

Bayesian Semiparametric Multivariate Change Point Analysis

We develop a general Bayesian semiparametric change-point model in which separate groups of parameters (for example, location and dispersion) can each follow a separate multiple change-point process, driven by time-dependent transition matrices among the latent regimes. The distribution of the observations within regimes dened by the various change-points is unknown and given by a Dirichlet process mixture prior. The prior-posterior analysis by Markov chain Monte Carlo techniques is developed on a multivariate forward-backward algorithm for sampling the various regime indicators

The Role of Managerial Control in Innovation Processes: an Empirical Analysis among Italian Firms

This paper investigates the link between the Managerial Control System (MCS) and product/service innovation. The aim is to provide an empirical analysis of the role of an MCS in innovation performance and, more specifically, to highlight the characteristics of an MCS that can better support the development of innovation. We refer to the implementation of formal or informal control mechanisms or, with regard to formal mechanisms, to how they are utilized by distinguishing between diagnostic and interactive use. The sample involved in this study consists of 104 Italian manufacturing firms belonging to those sectors of the Italian economy that feature the largest number of registered patents according to the European trend chart on innovation. The results show that an MCS can enhance product/service innovation but it can also inhibit it depending on the role it plays. Indeed, an MCS may hamper innovation if it is limited to pursuing diagnostic functions. Conversely, product/service innovation is positively associated to an interactive use of the MCS. In this case, it is an effective innovation driver capable of stimulating coordination, communication, and learning within innovation teams. The results of this study may have major implications for practitioners. Organizations hoping to enhance their innovation performance should strive to develop an MCS that can blend the monitoring activity with a driver to free thinking and the search for opportunities, while avoiding a strict compliance with rules and regulations, rigid performance evaluation and internal orientation

Marginal modeling of multilateral relational events

AbstractWe implement the methodology of marginal modeling of relational events involving groups of actors, as developed in Bartolucci, Peluso and Mira (2017). Current relational data analyses suffer from the representation of an event through edge variables, with potential loss of information when the events generate a set of multiple relations rather than bilateral connections or ties. To fully exploit the informational content of relational events, we model an event as a binary vector of response variables representing actors participating to the event. Univariate and bivariate distributions of the events are modeled through marginal parameters having a clear social interpretation