Signal Clustering Using Bayesian Inference (SCUBI): A Bayesian Approach to Choosing Consistent GNSS Signals

Abstract Summary Metastases cause a vast majority of cancer morbidity and mortality. Metastatic clones are formed by dispersal of cancer cells to secondary tissues, and are not medically detected or visible until later stages of cancer development. Clone phylogenies within patients provide a means of tracing the otherwise inaccessible dynamic history of migrations of cancer cells. Here, we present a new Bayesian approach, PathFinder, for reconstructing the routes of cancer cell migrations. PathFinder uses the clone phylogeny, the number of mutational differences among clones, and the information on the presence and absence of observed clones in primary and metastatic tumors. By analyzing simulated datasets, we found that PathFinder performes well in reconstructing clone migrations from the primary tumor to new metastases as well as between metastases. It was more challenging to trace migrations from metastases back to primary tumors. We found that a vast majority of errors can be corrected by sampling more clones per tumor, and by increasing the number of genetic variants assayed per clone. We also identified situations in which phylogenetic approaches alone are not sufficient to reconstruct migration routes. In conclusion, we anticipate that the use of PathFinder will enable a more reliable inference of migration histories and their posterior probabilities, which is required to assess the relative preponderance of seeding of new metastasis by clones from primary tumors and/or existing metastases. Availability and implementation PathFinder is available on the web at https://github.com/SayakaMiura/PathFinder.

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Bayesian Inference for the Difference of Two Proportion Parameters in Over-Reported Two-Sample Binomial Data Using the Doubly Sample

Stats ◽

10.3390/stats2010009 ◽

2019 ◽

Vol 2 (1) ◽

pp. 111-120 ◽

Cited By ~ 1

Author(s):

Dewi Rahardja

Keyword(s):

Bayesian Inference ◽

Closed Form ◽

Bayesian Approach ◽

Interval Estimation ◽

Real Data ◽

Simulation Studies ◽

Posterior Distributions ◽

Binomial Data ◽

The Difference ◽

Data Subject

We construct a point and interval estimation using a Bayesian approach for the difference of two population proportion parameters based on two independent samples of binomial data subject to one type of misclassification. Specifically, we derive an easy-to-implement closed-form algorithm for drawing from the posterior distributions. For illustration, we applied our algorithm to a real data example. Finally, we conduct simulation studies to demonstrate the efficiency of our algorithm for Bayesian inference.

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Bayesian approach to landing page testing

Problems of applied mathematics and mathematic modeling ◽

10.15421/321901 ◽

2019 ◽

Author(s):

Ya. S. Bondarenko ◽

S. V. Kravchenko

Keyword(s):

Bayesian Inference ◽

Loss Function ◽

Bayesian Approach ◽

Conversion Rate ◽

Expected Value ◽

Testing Methodology ◽

Rate Optimization

In this paper a Bayesian inference to conversion rate optimization is considered. Bayesian A/B/C testing methodology with the expected value of the loss function computed analytically is proposed. Bayesian A/B/C testing results are presented graphically and descriptively.

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Estimation of Flow-Accelerated Corrosion Rate in Nuclear Piping System

Journal of Nuclear Engineering and Radiation Science ◽

10.1115/1.4044407 ◽

2019 ◽

Vol 6 (1) ◽

Cited By ~ 1

Author(s):

Indranil Hazra ◽

Mahesh D. Pandey ◽

Mikko I. Jyrkama

Keyword(s):

Markov Chain ◽

Bayesian Inference ◽

Bayesian Approach ◽

Computational Time ◽

Piping System ◽

Linear Regression Method ◽

Bayesian Computation ◽

Accelerated Corrosion ◽

Computation Scheme ◽

Flow Accelerated Corrosion

Abstract Flow-accelerated corrosion (FAC) is a life-limiting factor for the piping network of the primary heat transport system (PHTS) in CANDU® reactors. The pipe wall thinning caused by FAC is monitored by carrying out periodic in-service inspections (ISI) to ensure the fitness-for-service of the piping system. Accurate prediction of the lifetime of various components in the PHTS piping network requires estimation of FAC thinning rate. The traditional Bayesian inference techniques commonly employed for parameter estimation are computationally costly. This paper presents an inexpensive and intuitive simulation-based Bayesian approach to FAC rate estimation, called approximate Bayesian computation using Markov chain Monte Carlo (ABC-MCMC). ABC-MCMC is a likelihood-free Bayesian computation scheme that generates samples directly from an approximate posterior distribution by simulating data sets from a forward model. The efficiency of ABC-MCMC is demonstrated by presenting a comparison with a likelihood-based Bayesian computation scheme, Metropolis-Hastings (MH) algorithm, using a practical data-based example. Furthermore, an innovative step has been proposed for reducing the Markov chain burn-in time in the proposed scheme. To indicate the need of a Bayesian approach in quantifying the uncertainties related to the FAC model parameters, results from the linear regression method, a common industrial approach, are also presented in this study. The numerical results show a notable reduction in computational time, suggesting that ABC-MCMC is an efficient alternative to the traditional Bayesian inference methods, specifically for handling noisy degradation data.

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Bayesian Inference for Comparative Research

American Political Science Review ◽

10.2307/2944713 ◽

1994 ◽

Vol 88 (2) ◽

pp. 412-423 ◽

Cited By ~ 97

Author(s):

Bruce Western ◽

Simon Jackman

Keyword(s):

Regression Analysis ◽

Comparative Analysis ◽

Bayesian Inference ◽

Statistical Inference ◽

Bayesian Approach ◽

Comparative Research ◽

Data Sets ◽

Explanatory Variables ◽

Long Run

Regression analysis in comparative research suffers from two distinct problems of statistical inference. First, because the data constitute all the available observations from a population, conventional inference based on the long-run behavior of a repeatable data mechanism is not appropriate. Second, the small and collinear data sets of comparative research yield imprecise estimates of the effects of explanatory variables. We describe a Bayesian approach to statistical inference that provides a unified solution to these two problems. This approach is illustrated in a comparative analysis of unionization.

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Using demographics toward efficient data classification in citizen science: a Bayesian approach

PeerJ Computer Science ◽

10.7717/peerj-cs.239 ◽

2019 ◽

Vol 5 ◽

pp. e239

Author(s):

Pietro De Lellis ◽

Shinnosuke Nakayama ◽

Maurizio Porfiri

Keyword(s):

Bayesian Inference ◽

Citizen Science ◽

Bayesian Approach ◽

Prior Information ◽

Data Accuracy ◽

Majority Voting ◽

Accurate Information ◽

Training Set ◽

Level Of Education ◽

Efficient Data

Public participation in scientific activities, often called citizen science, offers a possibility to collect and analyze an unprecedentedly large amount of data. However, diversity of volunteers poses a challenge to obtain accurate information when these data are aggregated. To overcome this problem, we propose a classification algorithm using Bayesian inference that harnesses diversity of volunteers to improve data accuracy. In the algorithm, each volunteer is grouped into a distinct class based on a survey regarding either their level of education or motivation to citizen science. We obtained the behavior of each class through a training set, which was then used as a prior information to estimate performance of new volunteers. By applying this approach to an existing citizen science dataset to classify images into categories, we demonstrate improvement in data accuracy, compared to the traditional majority voting. Our algorithm offers a simple, yet powerful, way to improve data accuracy under limited effort of volunteers by predicting the behavior of a class of individuals, rather than attempting at a granular description of each of them.

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A Bayesian Approach to Argument-Based Reasoning for Attack Estimation

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/36 ◽

2017 ◽

Cited By ~ 3

Author(s):

Hiroyuki Kido ◽

Keishi Okamoto

Keyword(s):

Bayesian Inference ◽

Bayesian Network ◽

Network Model ◽

Bayesian Approach ◽

Online Forums ◽

Bayesian Network Model ◽

Abstract Argumentation ◽

The Web

The web is a source of a large amount of arguments and their acceptability statuses (e.g., votes for and against the arguments). However, relations existing between the fore-mentioned arguments are typically not available. This study investigates the utilisation of acceptability semantics to statistically estimate an attack relation between arguments wherein the acceptability statuses of arguments are provided. A Bayesian network model of argument-based reasoning is defined in which Dung's theory of abstract argumentation gives the substance of Bayesian inference. The model correctness is demonstrated by analysing properties of estimated attack relations and illustrating its applicability to online forums.

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Bayesian Inference for Milling Stability Using a Random Walk Approach

Journal of Manufacturing Science and Engineering ◽

10.1115/1.4027226 ◽

2014 ◽

Vol 136 (3) ◽

Cited By ~ 6

Author(s):

Jaydeep Karandikar ◽

Michael Traverso ◽

Ali Abbas ◽

Tony Schmitz

Keyword(s):

Random Walk ◽

Bayesian Inference ◽

Probability Distribution ◽

Bayesian Approach ◽

Stability Boundary ◽

Value Added ◽

Experimental Results ◽

Depth Of Cut ◽

Machining Parameters ◽

The Stability

Unstable cutting conditions limit the profitability in milling. While analytical and numerical approaches for estimating the limiting axial depth of cut as a function of spindle speed are available, they are generally deterministic in nature. Because uncertainty inherently exists, a Bayesian approach that uses a random walk strategy for establishing a stability model is implemented in this work. The stability boundary is modeled using random walks. The probability of the random walk being the true stability limit is then updated using experimental results. The stability test points are identified using a value of information method. Bayesian inference offers several advantages including the incorporation of uncertainty in the model using a probability distribution (rather than deterministic value), updating the probability distribution using new experimental results, and selecting the experiments such that the expected value added by performing the experiment is maximized. Validation of the Bayesian approach is presented. The experimental results show a convergence to the optimum machining parameters for milling a pocket without prior knowledge of the system dynamics.

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Bayesian Inference for Skew-Symmetric Distributions

Symmetry ◽

10.3390/sym12040491 ◽

2020 ◽

Vol 12 (4) ◽

pp. 491

Author(s):

Fatemeh Ghaderinezhad ◽

Christophe Ley ◽

Nicola Loperfido

Keyword(s):

Bayesian Inference ◽

Bayesian Approach ◽

Symmetric Distributions ◽

Prior Distributions ◽

Univariate Case ◽

Frequentist Inference ◽

Skew Normal

Skew-symmetric distributions are a popular family of flexible distributions that conveniently model non-normal features such as skewness, kurtosis and multimodality. Unfortunately, their frequentist inference poses several difficulties, which may be adequately addressed by means of a Bayesian approach. This paper reviews the main prior distributions proposed for the parameters of skew-symmetric distributions, with special emphasis on the skew-normal and the skew-t distributions which are the most prominent skew-symmetric models. The paper focuses on the univariate case in the absence of covariates, but more general models are also discussed.

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PathFinder: Bayesian inference of clone migration histories in cancer

10.1101/2020.07.10.197194 ◽

2020 ◽

Author(s):

Sudhir Kumar ◽

Antonia Chroni ◽

Koichiro Tamura ◽

Maxwell Sanderford ◽

Olumide Oladeinde ◽

...

Keyword(s):

Bayesian Inference ◽

Cancer Cells ◽

Bayesian Approach ◽

Genetic Variants ◽

Cancer Development ◽

Posterior Probabilities ◽

Migration Routes ◽

Primary Tumors ◽

History Of ◽

The Web

AbstractSummaryMetastases form by dispersal of cancer cells to secondary tissues. They cause a vast majority of cancer morbidity and mortality. Metastatic clones are not medically detected or visible until later stages of cancer development. Thus, clone phylogenies within patients provide a means of tracing the otherwise inaccessible dynamic history of migrations of cancer cells. Here we present a new Bayesian approach, PathFinder, for reconstructing the routes of cancer cell migrations. PathFinder uses the clone phylogeny and the numbers of mutational differences among clones, along with the information on the presence and absence of observed clones in different primary and metastatic tumors. In the analysis of simulated datasets, PathFinder performed well in reconstructing migrations from the primary tumor to new metastases as well as between metastases. However, it was much more challenging to trace migrations from metastases back to primary tumors. We found that a vast majority of errors can be corrected by sampling more clones per tumor and by increasing the number of genetic variants assayed. We also identified situations in which phylogenetic approaches alone are not sufficient to reconstruct migration routes.ConclusionsWe anticipate that the use of PathFinder will enable a more reliable inference of migration histories, along with their posterior probabilities, which is required to assess the relative preponderance of seeding of new metastasis by clones from primary tumors and/or existing metastases.AvailabilityPathFinder is available on the web at https://github.com/SayakaMiura/[email protected]

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