Statistical analysis of sheep CAT scan images using a Bayesian mixture model

2004 ◽  
Vol 55 (1) ◽  
pp. 57 ◽  
Author(s):  
C. L. Alston ◽  
K. L. Mengersen ◽  
J. M. Thompson ◽  
P. J. Littlefield ◽  
D. Perry ◽  
...  
Keyword(s):  
Animal Experiments ◽  
Finite Mixture ◽  
Lean Tissue ◽  
Gaussian Distributions ◽  
Mixture Modelling ◽  
Bayesian Mixture Model ◽  
Cat Scan ◽  
Distributional Properties ◽  
Bayesian Formulation ◽  
Bayesian Mixture

CAT scanning techniques are available to provide images that can aid in the assessment of carcass traits in live sheep during the course of animal experiments. In this paper we present a Bayesian formulation of an analysis that allows us to determine the composition of a scan in terms of proportions of the image attributable to fat, muscle (lean tissue), and bone. The technique, known as finite mixture modelling, also provides information about the distributional properties of some of these components, such as fat and bone. In the case of muscle, the analysis estimates several Gaussian distributions that combine to provide an approximation to its likelihood.The model is estimated through the use of the Gibbs sampler, with the distributional properties of carcass components being obtained from the resultant Markov chains.

scholarly journals Investment Modelling Using Value at Risk Bayesian Mixture Modelling Approach and Backtesting to Assess Stock Risk

2021 ◽  
Vol 7 (1) ◽  
pp. 11
Author(s):  
Brina Miftahurrohmah ◽  
Catur Wulandari ◽  
Yogantara Setya Dharmawan
Keyword(s):  
At Risk ◽  
Mixture Model ◽  
Value At Risk ◽  
Var Model ◽  
Mixture Modelling ◽  
Bayesian Mixture Model ◽  
Stock Investment ◽  
Bayesian Mixture ◽  
Heavy Tailed ◽  
The One

Background: Stock investment has been gaining momentum in the past years due to the development of technology. During the pandemic lockdown, people have invested more. One the one hand, stock investment has high potential profitability, but on the other, it is equally risky. Therefore, a value at risk (VaR) analysis is needed. One approach to calculate VaR is by using the Bayesian mixture model, which has been proven to be able to overcome heavy-tailed cases. Then, the VaR’s accuracy needs to be tested, and one of the ways is by using backtesting, such as the Kupiec test.Objective: This study aims to determine the VaR model of PT NFC Indonesia Tbk (NFCX) return data using Bayesian mixture modelling and backtesting. On a practical level, this study can provide information about the potential risks of investing that is grounded in empirical evidence.Methods: The data used was NFCX data retrieved from Yahoo Finance, which was then modelled with a mixture model based on the normal and Laplace distributions. After that, the VaR accuracy was calculated and then tested by using backtesting.Results: The test results showed that the VaR with the mixture Laplace autoregressive (MLAR) approach (2;[2],[4]) was accurate at 5% and 1% quantiles while mixture normal autoregressive MNAR (2;[2],[2,4]) was only accurate at 5% quantiles.Conclusion: The better performing NFCX VaR model for this study based on backtesting using Kupiec test is MLAR(2;[2],[4]).

Extending the Bayesian mixture model to incorporate spatial information in analysing sheep CAT scan images

2005 ◽  
Vol 56 (4) ◽  
pp. 373 ◽  
Author(s):  
C. L. Alston ◽  
K. L. Mengersen ◽  
J. M. Thompson ◽  
P. J. Littlefield ◽  
D. Perry ◽  
...  
Keyword(s):  
Mixture Model ◽  
Spatial Information ◽  
Normal Mixture ◽  
Animal Science ◽  
Normal Mixture Model ◽  
Bayesian Mixture Model ◽  
Cat Scan ◽  
Bayesian Mixture ◽  
Component Allocation ◽  
Separation Of Components

The purpose of CAT scanning in some animal science experiments is to provide estimates of the proportion of the tissues, fat, muscle, and bone present in an individual body, and compare some of the density characteristics. In this paper we present an extension to the hierarchical Bayesian Normal mixture model, which incorporates some of the information provided by the neighbouring pixels in a CAT scan image. This neighbour information is included in the model through the use of a Markov random field for the component allocation variable. This extended mixture model provides a more responsive fit to the local likelihood of the data than that of the independent mixture model. The effectiveness of this modelling technique is illustrated by comparing its performance with that of a Normal mixture model and a fixed boundary method in 3 examples. In these examples it is shown that the extended mixture model we propose is most useful in situations that involve only slight separation of components. The advantages of the model decline as the separation of components increases.

scholarly journals Bayesian Mixture Modelling for Mortality Projection

Risks ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 76
Author(s):  
Jackie Li ◽  
Atsuyuki Kogure
Keyword(s):  
Bayesian Approach ◽  
Model Uncertainty ◽  
Finite Mixture Model ◽  
Finite Mixture ◽  
Model Concept ◽  
Projection Model ◽  
Mixture Modelling ◽  
Mortality Projection ◽  
Bayesian Mixture ◽  
Model Structures

Although a large number of mortality projection models have been proposed in the literature, relatively little attention has been paid to a formal assessment of the effect of model uncertainty. In this paper, we construct a Bayesian framework for embedding more than one mortality projection model and utilise the finite mixture model concept to allow for the blending of model structures. Under this framework, the varying features of different model structures can be exploited jointly and coherently to have a more detailed description of the underlying mortality patterns. We show that the proposed Bayesian approach performs well in fitting and forecasting Japanese mortality.

scholarly journals A Finite Mixture Modelling Perspective for Combining Experts’ Opinions with an Application to Quantile-Based Risk Measures

Risks ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 115
Author(s):  
Despoina Makariou ◽  
Pauline Barrieu ◽  
George Tzougas
Keyword(s):  
Decision Making ◽  
Mixture Models ◽  
Finite Mixture Models ◽  
Risk Measures ◽  
Finite Mixture ◽  
Parametric Family ◽  
Collective Decision Making ◽  
Multiple Sources ◽  
Mixture Modelling ◽  
Expert Opinions

The key purpose of this paper is to present an alternative viewpoint for combining expert opinions based on finite mixture models. Moreover, we consider that the components of the mixture are not necessarily assumed to be from the same parametric family. This approach can enable the agent to make informed decisions about the uncertain quantity of interest in a flexible manner that accounts for multiple sources of heterogeneity involved in the opinions expressed by the experts in terms of the parametric family, the parameters of each component density, and also the mixing weights. Finally, the proposed models are employed for numerically computing quantile-based risk measures in a collective decision-making context.

App2Vec: Context-Aware Application Usage Prediction

2021 ◽  
Vol 15 (6) ◽  
pp. 1-21
Author(s):  
Huandong Wang ◽  
Yong Li ◽  
Mu Du ◽  
Zhenhui Li ◽  
Depeng Jin
Keyword(s):  
Dirichlet Process ◽  
Service Providers ◽  
State Of The Art ◽  
Representation Learning ◽  
Context Aware ◽  
Challenging Problem ◽  
Performance Gap ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture ◽  
Spatio Temporal

Both app developers and service providers have strong motivations to understand when and where certain apps are used by users. However, it has been a challenging problem due to the highly skewed and noisy app usage data. Moreover, apps are regarded as independent items in existing studies, which fail to capture the hidden semantics in app usage traces. In this article, we propose App2Vec, a powerful representation learning model to learn the semantic embedding of apps with the consideration of spatio-temporal context. Based on the obtained semantic embeddings, we develop a probabilistic model based on the Bayesian mixture model and Dirichlet process to capture when , where , and what semantics of apps are used to predict the future usage. We evaluate our model using two different app usage datasets, which involve over 1.7 million users and 2,000+ apps. Evaluation results show that our proposed App2Vec algorithm outperforms the state-of-the-art algorithms in app usage prediction with a performance gap of over 17.0%.

scholarly journals Three years of Sun-as-a-star radial-velocity observations on the approach to solar minimum

2019 ◽  
Vol 487 (1) ◽  
pp. 1082-1100 ◽  
Author(s):  
A Collier Cameron ◽  
A Mortier ◽  
D Phillips ◽  
X Dumusque ◽  
R D Haywood ◽  
...  
Keyword(s):  
Radial Velocity ◽  
Zero Point ◽  
Spatially Resolved ◽  
Solar Observations ◽  
Bayesian Mixture Model ◽  
Mixture Model Approach ◽  
Calibration Uncertainty ◽  
Bayesian Mixture ◽  
Solar Type ◽  
Line Asymmetry

Abstract The time-variable velocity fields of solar-type stars limit the precision of radial-velocity determinations of their planets’ masses, obstructing detection of Earth twins. Since 2015 July, we have been monitoring disc-integrated sunlight in daytime using a purpose-built solar telescope and fibre feed to the HARPS-N stellar radial-velocity spectrometer. We present and analyse the solar radial-velocity measurements and cross-correlation function (CCF) parameters obtained in the first 3 yr of observation, interpreting them in the context of spatially resolved solar observations. We describe a Bayesian mixture-model approach to automated data-quality monitoring. We provide dynamical and daily differential-extinction corrections to place the radial velocities in the heliocentric reference frame, and the CCF shape parameters in the sidereal frame. We achieve a photon-noise-limited radial-velocity precision better than 0.43 m s−1 per 5-min observation. The day-to-day precision is limited by zero-point calibration uncertainty with an RMS scatter of about 0.4 m s−1. We find significant signals from granulation and solar activity. Within a day, granulation noise dominates, with an amplitude of about 0.4 m s−1 and an autocorrelation half-life of 15 min. On longer time-scales, activity dominates. Sunspot groups broaden the CCF as they cross the solar disc. Facular regions temporarily reduce the intrinsic asymmetry of the CCF. The radial-velocity increase that accompanies an active-region passage has a typical amplitude of 5 m s−1 and is correlated with the line asymmetry, but leads it by 3 d. Spectral line-shape variability thus shows promise as a proxy for recovering the true radial velocity.

scholarly journals Bayesian Mixture Model Analysis for Detecting Differentially Expressed Genes

2008 ◽  
Vol 2008 ◽  
pp. 1-12 ◽  
Author(s):  
Zhenyu Jia ◽  
Shizhong Xu
Keyword(s):  
Mixture Model ◽  
Differentially Expressed Genes ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Control Treatment ◽  
Microarray Gene Expression ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture ◽  
Truncated Normal ◽  
Upregulated Genes

Control-treatment design is widely used in microarray gene expression experiments. The purpose of such a design is to detect genes that express differentially between the control and the treatment. Many statistical procedures have been developed to detect differentially expressed genes, but all have pros and cons and room is still open for improvement. In this study, we propose a Bayesian mixture model approach to classifying genes into one of three clusters, corresponding to clusters of downregulated, neutral, and upregulated genes, respectively. The Bayesian method is implemented via the Markov chain Monte Carlo (MCMC) algorithm. The cluster means of down- and upregulated genes are sampled from truncated normal distributions whereas the cluster mean of the neutral genes is set to zero. Using simulated data as well as data from a real microarray experiment, we demonstrate that the new method outperforms all methods commonly used in differential expression analysis.

Bayesian Mixture Model for Prediction of Bus Arrival Time

2015 ◽  
Vol 6 (6) ◽  
pp. 961
Author(s):  
Misbahuddin Misbahuddin ◽  
Riri Fitri Sari
Keyword(s):  
Mixture Model ◽  
Arrival Time ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture
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