scholarly journals Bayesian Testing for Exogenous Partition Structures in Stochastic Block Models

Sankhya A ◽  
2020 ◽  
Author(s):  
Sirio Legramanti ◽  
Tommaso Rigon ◽  
Daniele Durante
Keyword(s):  
Bayes Factor ◽  
Testing Procedure ◽  
Relational Model ◽  
Cluster Assignment ◽  
Additional Information ◽  
Bayesian Testing ◽  
Stochastic Equivalence ◽  
External Node ◽  
Block Models ◽  
Block Structures

AbstractNetwork data often exhibit block structures characterized by clusters of nodes with similar patterns of edge formation. When such relational data are complemented by additional information on exogenous node partitions, these sources of knowledge are typically included in the model to supervise the cluster assignment mechanism or to improve inference on edge probabilities. Although these solutions are routinely implemented, there is a lack of formal approaches to test if a given external node partition is in line with the endogenous clustering structure encoding stochastic equivalence patterns among the nodes in the network. To fill this gap, we develop a formal Bayesian testing procedure which relies on the calculation of the Bayes factor between a stochastic block model with known grouping structure defined by the exogenous node partition and an infinite relational model that allows the endogenous clustering configurations to be unknown, random and fully revealed by the block–connectivity patterns in the network. A simple Markov chain Monte Carlo method for computing the Bayes factor and quantifying uncertainty in the endogenous groups is proposed. This strategy is evaluated in simulations, and in applications studying brain networks of Alzheimer’s patients.

scholarly journals On the white, the black, and the many shades of gray in between: Our reply to van Ravenzwaaij and Wagenmakers (2021)

2021 ◽  
Author(s):  
Jorge Tendeiro ◽  
Henk Kiers
Keyword(s):  
Null Hypothesis ◽  
Bayes Factor ◽  
Critical Appraisal ◽  
Bayesian Testing ◽  
The Many

In 2019 we wrote a paper (Tendeiro & Kiers, 2019) in Psychological Methods over null hypothesis Bayesian testing and its working horse, the Bayes factor. Recently, van Ravenzwaaij and Wagenmakers (2021) offered a response to our piece, also in this journal. Although we do welcome their contribution with thought-provoking remarks on our paper, we ended up concluding that there were too many ‘issues’ in van Ravenzwaaij and Wagenmakers (2021) that warrant a rebuttal. In this paper we both defend the main premises of our original paper and we put the contribution of van Ravenzwaaij and Wagenmakers (2021) under critical appraisal. Our hope is that this exchange between scholars decisively contributes towards a better understanding among psychologists of null hypothesis Bayesian testing in general and of the Bayes factor in particular.

scholarly journals Effectiveness of laboratory practical for Students’ Learning

2019 ◽  
Vol 7 (3) ◽  
pp. 222-236
Author(s):  
G.W. Tesila Chandrakanthi Kandamby
Keyword(s):  
Civil Engineering ◽  
Testing Procedure ◽  
Learning Effectiveness ◽  
Learn Theory ◽  
Active Involvement ◽  
Additional Information ◽  
Hands On ◽  
One Year ◽  
Almost All ◽  
Practical Task

Modules relating to engineering disciplines mostly comprise laboratory hands on practical in order to demonstrate the application of theory in practice. Guided sheet is usually followed by the instructor while carrying out the practical and students are allowed to work as a team by following the instructions. Since it is a common practice in almost all engineering laboratories, students’ learning was investigated using two soil experiments in civil engineering technological programme in 2018. Interviews were conducted to search what students learn from the practical by recalling learned materials from sample of students after completion of the practical and the method adopted by the instructors were collected through the questionnaire. Analysis based on recalling learning showed that students remember observable aspects of practical task such as identification of apparatus and the testing procedure within one year but it does not assist them to learn theory and calculations though it has been totally covered during the practical lesson. It is noted that students highly involved in doing practical in laboratory rather than attending theory and calculation. Students’ active involvement in learning before the commencement of practical with the assistance of the instructor, observing physical outcomes while doing and searching additional information at the end through internet have showed better results. Preset process is found partially effective and learning on theory and calculation need to be improved to make the process success. Keywords: Laboratory practical, students’ learning, recalling learning, effectiveness,

scholarly journals Advantages Masquerading as ‘Issues’ in Bayesian Hypothesis Testing: A Commentary on Tendeiro and Kiers (2019)

2019 ◽  
Author(s):  
Don van Ravenzwaaij ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Hypothesis Testing ◽  
Null Hypothesis ◽  
Bayes Factor ◽  
Statistical Evidence ◽  
Central Component ◽  
P Values ◽  
Bayesian Hypothesis Testing ◽  
Bayesian Testing

Tendeiro and Kiers (2019) provide a detailed and scholarly critique of Null Hypothesis Bayesian Testing (NHBT) and its central component –the Bayes factor– that allows researchers to update knowledge and quantify statistical evidence. Tendeiro and Kiers conclude that NHBT constitutes an improvement over frequentist p-values, but primarily elaborate on a list of eleven ‘issues’ of NHBT. In this commentary, we provide context to each issue and conclude that many issues may in fact be conceived as pronounced advantages of NHBT.

scholarly journals A Global Bayes Factor for Observations on an Infinite-Dimensional Hilbert Space, Applied to Signal Detection in fMRI

2021 ◽  
Vol 50 (3) ◽  
pp. 66-76
Author(s):  
Khalil Shafie ◽  
Mohammad Reza  Faridrohani ◽  
Siamak Noorbaloochi ◽  
Hossein Moradi Rekabdarkolaee
Keyword(s):  
Hilbert Space ◽  
Signal Detection ◽  
Bayes Factor ◽  
Real Data ◽  
Testing Procedure ◽  
Brain Regions ◽  
Infinite Dimensional ◽  
Infinite Dimensional Hilbert Space ◽  
Dimensional Hilbert Space ◽  
Definition Of

Functional Magnetic Resonance Imaging (fMRI) is a fundamental tool in advancing our understanding of the brain's functionality. Recently, a series of Bayesian approaches have been suggested to test for the voxel activation in different brain regions. In this paper, we propose a novel definition for the global Bayes factor to test for activation using the Radon-Nikodym derivative. Our proposed method extends the definition of Bayes factor to an infinite dimensional Hilbert space. Using this extended definition, a Bayesian testing procedure is introduced for signal detection in noisy images when both signal and noise are considered as an element of an infinite dimensional Hilbert space. This new approach is illustrated through a real data analysis to find activated areas of Brain in an fMRI data.

scholarly journals Mathematical Evidence for the Adequacy of Bayesian Optional Stopping

2019 ◽  
Author(s):  
Jorge Tendeiro ◽  
Henk Kiers ◽  
Don van Ravenzwaaij
Keyword(s):  
Null Hypothesis ◽  
Bayes Factor ◽  
Random Variable ◽  
P Values ◽  
Significance Level ◽  
Bayesian Testing ◽  
Optional Stopping ◽  
Repeated Sampling ◽  
State Of Affairs ◽  
Approximate Simulation

Description: The practice of sequentially testing a null hypothesis as data are collected until the null hypothesis is rejected is known as optional stopping. It is well-known that optional stopping is problematic in the context of null hypothesis significance testing: The false positive rates quickly overcome the single test’s significance level. However, the state of affairs under null hypothesis Bayesian testing, where p-values are replaced by Bayes factors, is perhaps surprisingly much less consensual. Rouder (2014) used simulations to defend the use of optional stopping under null hypothesis Bayesian testing. The idea behind these simulations is closely related to the idea of sampling from prior predictive distributions. In this paper we provide formal mathematical derivations for Rouder’s approximate simulation results for the two Bayesian hypothesis tests that he considered. The key idea is to consider the probability distribution of the Bayes factor, which is regarded as being a random variable across repeated sampling. This paper therefore offers a solid mathematical footing to the literature and we believe it is a valid contribution towards understanding the practice of optional stopping in the context of Bayesian hypothesis testing.

The Basis for Masonry Analysis with UDEC and 3DEC

2016 ◽  
pp. 61-89 ◽  
Author(s):  
José V. Lemos
Keyword(s):  
Distinct Element ◽  
Nonlinear Behavior ◽  
Solution Algorithms ◽  
Stone Block ◽  
Circular Particle ◽  
Highly Nonlinear ◽  
Key Issues ◽  
Block Models ◽  
Block Structures

The “distinct element method” was proposed by Peter Cundall in 1971 for the analysis of rock slopes by means of rigid block or circular particle models. This method led to the UDEC and 3DEC codes, presently in wide use in rock engineering. Their application to masonry structures started in the 90's, as researchers found that they were also excellent tools to approach the highly nonlinear behavior of masonry, in particular the collapse processes of stone block structures under static or seismic loads. This chapter reviews the essential assumptions of UDEC and 3DEC, relating them to other methods and codes, and stressing the features that make them suitable for masonry analysis. Rigid and deformable blocks, contact mechanics, contact detection, and solution algorithms are examined. Key issues in the modelling of masonry are addressed, including: irregular block models; determination of collapse loads; large displacement analysis; computational efficiency issues in dynamic analysis. Practical examples taken from the published literature illustrate these issues.

Production of New and Revised A.A.V.S.O. Charts

1979 ◽  
Vol 46 ◽  
pp. 368
Author(s):  
Clinton B. Ford
Keyword(s):  
Variable Star ◽  
Standard Form ◽  
Original Data ◽  
University Of Pennsylvania ◽  
Standard Format ◽  
Additional Information ◽  
The Gift ◽  
The University

A “new charts program” for the Americal Association of Variable Star Observers was instigated in 1966 via the gift to the Association of the complete variable star observing records, charts, photographs, etc. of the late Prof. Charles P. Olivier of the University of Pennsylvania (USA). Adequate material covering about 60 variables, not previously charted by the AAVSO, was included in this original data, and was suitably charted in reproducible standard format.Since 1966, much additional information has been assembled from other sources, three Catalogs have been issued which list the new or revised charts produced, and which specify how copies of same may be obtained. The latest such Catalog is dated June 1978, and lists 670 different charts covering a total of 611 variables none of which was charted in reproducible standard form previous to 1966.

Convergent Beam Electron Diffraction

1978 ◽  
Vol 36 (3) ◽  
pp. 304-315
Author(s):  
G. Lehmpfuhl
Keyword(s):  
Electron Diffraction ◽  
Diffraction Pattern ◽  
Crystal Surface ◽  
Diffraction Spot ◽  
Crystal Thickness ◽  
Large Area ◽  
Electron Microscopic ◽  
Additional Information ◽  
Microscopic Investigations

Introduction In electron microscopic investigations of crystalline specimens the direct observation of the electron diffraction pattern gives additional information about the specimen. The quality of this information depends on the quality of the crystals or the crystal area contributing to the diffraction pattern. By selected area diffraction in a conventional electron microscope, specimen areas as small as 1 µ in diameter can be investigated. It is well known that crystal areas of that size which must be thin enough (in the order of 1000 Å) for electron microscopic investigations are normally somewhat distorted by bending, or they are not homogeneous. Furthermore, the crystal surface is not well defined over such a large area. These are facts which cause reduction of information in the diffraction pattern. The intensity of a diffraction spot, for example, depends on the crystal thickness. If the thickness is not uniform over the investigated area, one observes an averaged intensity, so that the intensity distribution in the diffraction pattern cannot be used for an analysis unless additional information is available.

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