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Artículos de revistas sobre el tema "Bayesian non-Parametric model"

Autor: Grafiati

Publicado: 25 de mayo de 2024

Última modificación: 31 de julio de 2025

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1

Assaf, A. George, Mike Tsionas, Florian Kock, and Alexander Josiassen. "A Bayesian non-parametric stochastic frontier model." Annals of Tourism Research 87 (March 2021): 103116. http://dx.doi.org/10.1016/j.annals.2020.103116.

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2

Assaf, A. George, Mike Tsionas, Florian Kock, and Alexander Josiassen. "A Bayesian non-parametric stochastic frontier model." Annals of Tourism Research 87 (March 2021): 103116. http://dx.doi.org/10.1016/j.annals.2020.103116.

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3

LI, R., J. ZHOU, and L. WANG. "ESTIMATION OF THE BINARY LOGISTIC REGRESSION MODEL PARAMETER USING BOOTSTRAP RE-SAMPLING." Latin American Applied Research - An international journal 48, no. 3 (2018): 199–204. http://dx.doi.org/10.52292/j.laar.2018.228.

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In this paper, the non-parametric bootstrap and non-parametric Bayesian bootstrap methods are applied for parameter estimation in the binary logistic regression model. A real data study and a simulation study are conducted to compare the Nonparametric bootstrap, Non-parametric Bayesian bootstrap and the maximum likelihood methods. Study results shows that three methods are all effective ways for parameter estimation in the binary logistic regression model. In small sample case, the non-parametric Bayesian bootstrap method performs relatively better than the non-parametric bootstrap and the max

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4

Alamri, Faten S., Edward L. Boone, and David J. Edwards. "A Bayesian Monotonic Non-parametric Dose-Response Model." Human and Ecological Risk Assessment: An International Journal 27, no. 8 (2021): 2104–23. http://dx.doi.org/10.1080/10807039.2021.1956298.

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5

Minh Nguyen, Thanh, and Q. M. Jonathan Wu. "A non-parametric Bayesian model for bounded data." Pattern Recognition 48, no. 6 (2015): 2084–95. http://dx.doi.org/10.1016/j.patcog.2014.12.019.

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6

Xia, Yunqing. "Application of non parametric Bayesian methods in high dimensional data." Journal of Computational Methods in Sciences and Engineering 24, no. 2 (2024): 731–43. http://dx.doi.org/10.3233/jcm-237104.

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With the development of technology and the widespread collection of data, high-dimensional data analysis has become a research hotspot in many fields. Traditional parameter methods often face problems such as dimensional disasters in high-dimensional data analysis. Non parametric methods have broad application prospects in high-dimensional data because they do not rely on specific parameter distribution assumptions. The Bayesian rule is more suitable for dealing with noise and outliers in high-dimensional data because it takes uncertainty into account. Therefore, it is of great significance to

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7

Dong, Alice X. D., Jennifer S. K. Chan, and Gareth W. Peters. "RISK MARGIN QUANTILE FUNCTION VIA PARAMETRIC AND NON-PARAMETRIC BAYESIAN APPROACHES." ASTIN Bulletin 45, no. 3 (2015): 503–50. http://dx.doi.org/10.1017/asb.2015.8.

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AbstractWe develop quantile functions from regression models in order to derive risk margin and to evaluate capital in non-life insurance applications. By utilizing the entire range of conditional quantile functions, especially higher quantile levels, we detail how quantile regression is capable of providing an accurate estimation of risk margin and an overview of implied capital based on the historical volatility of a general insurers loss portfolio. Two modeling frameworks are considered based around parametric and non-parametric regression models which we develop specifically in this insura

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8

Li, Hong, and Yang Lu. "A Bayesian non-parametric model for small population mortality." Scandinavian Actuarial Journal 2018, no. 7 (2018): 605–28. http://dx.doi.org/10.1080/03461238.2017.1418420.

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9

Abdulsamad Habeeb *, Ahmed, and Qutaiba N. Nayef Al-Kazaz. "A comparison between Speckman and Bayesian estimation method of a semiparametric balanced longitudinal data model." Journal of Economics and Administrative Sciences 30, no. 142 (2024): 449–64. http://dx.doi.org/10.33095/kpscqv37.

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This paper aims to use semi-parametric regression to balanced longitudinal data model, where the parametric regression models suffer from the problem of strict constraints, while non-parametric regression models, despite their flexibility, suffer from the problem of the curse of dimensionality. Consequently, semi-parametric regression is an ideal solution to get rid of the problems that parametric and non-parametric regression suffer from. The great advantage of this model is that it contains all the positive features included in the previous two models, such as containing strict restrictions

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10

MILADINOVIC, BRANKO, and CHRIS P. TSOKOS. "SENSITIVITY OF THE BAYESIAN RELIABILITY ESTIMATES FOR THE MODIFIED GUMBEL FAILURE MODEL." International Journal of Reliability, Quality and Safety Engineering 16, no. 04 (2009): 331–41. http://dx.doi.org/10.1142/s0218539309003423.

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The classical Gumbel probability distribution is modified in order to study the failure times of a given system. Bayesian estimates of the reliability function under five different parametric priors and the square error loss are studied. The Bayesian reliability estimate under the non-parametric kernel density prior is compared with those under the parametric priors and numerical computations are given to study their effectiveness.

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11

Habeeb, Ahmed Abdulsamad, and Qutaiba N. Nayef Al-Kazaz. "Bayesian and Classical Semi-parametric Estimation of the Balanced Longitudinal Data Model." International Academic Journal of Social Sciences 10, no. 2 (2023): 25–38. http://dx.doi.org/10.9756/iajss/v10i2/iajss1010.

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The primary objective of this study is to employ semi-parametric regression techniques in the balanced longitudinal data model. Where the parametric regression models are plagued by the problem of strict constraints, while non-parametric regression models, despite their flexibility, suffer from the problem of the curse of dimensionality. Consequently, semi-parametric regression presents a suitable solution to address the problems in parametric and non-parametric regression methods. The advantage of this model is that it contains all the positive properties included in the previous two models s

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12

Kim, Steven B., Scott M. Bartell, and Daniel L. Gillen. "Inference for the existence of hormetic dose–response relationships in toxicology studies." Biostatistics 17, no. 3 (2016): 523–36. http://dx.doi.org/10.1093/biostatistics/kxw004.

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Abstract In toxicology studies hormesis refers to a dose–response relationship with a stimulatory response at low doses and an inhibitory response at high doses. In this manuscript, we particularly focus on a J-shaped dose–response relationship for binary cancer responses. We propose and examine two new flexible models for testing the hypothesis of hormesis in a Bayesian framework. The first model is parametric and enhances the flexibility of modeling a hormetic zone by using a non-linear predictor in a multistage model. The second model is non-parametric and allows multiple model specificatio

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13

Tonner, Peter D., Cynthia L. Darnell, Francesca M. L. Bushell, Peter A. Lund, Amy K. Schmid, and Scott C. Schmidler. "A Bayesian non-parametric mixed-effects model of microbial growth curves." PLOS Computational Biology 16, no. 10 (2020): e1008366. http://dx.doi.org/10.1371/journal.pcbi.1008366.

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Substantive changes in gene expression, metabolism, and the proteome are manifested in overall changes in microbial population growth. Quantifying how microbes grow is therefore fundamental to areas such as genetics, bioengineering, and food safety. Traditional parametric growth curve models capture the population growth behavior through a set of summarizing parameters. However, estimation of these parameters from data is confounded by random effects such as experimental variability, batch effects or differences in experimental material. A systematic statistical method to identify and correct

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14

Kalinina, Irina A., and Aleksandr P. Gozhyj. "Modeling and forecasting of nonlinear nonstationary processes based on the Bayesian structural time series." Applied Aspects of Information Technology 5, no. 3 (2022): 240–55. http://dx.doi.org/10.15276/aait.05.2022.17.

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The article describes an approach to modelling and forecasting non-linear non-stationary time series for various purposes using Bayesian structural time series. The concepts of non-linearity and non-stationarity, as well as methods for processing non-linearity’sand non-stationarity in the construction of forecasting models are considered. The features of the Bayesian approach in the processing of nonlinearities and nonstationaryare presented. An approach to the construction of probabilistic-statistical models based on Bayesian structural models of time series has been studied. Parametric and n

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15

Hong, Liang, and Ryan Martin. "Real-time Bayesian non-parametric prediction of solvency risk." Annals of Actuarial Science 13, no. 1 (2018): 67–79. http://dx.doi.org/10.1017/s1748499518000039.

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AbstractInsurance regulation often dictates that insurers monitor their solvency risk in real time and take appropriate actions whenever the risk exceeds their tolerance level. Bayesian methods are appealing for prediction problems thanks to their ability to naturally incorporate both sample variability and parameter uncertainty into a predictive distribution. However, handling data arriving in real time requires a flexible non-parametric model, and the Monte Carlo methods necessary to evaluate the predictive distribution in such cases are not recursive and can be too expensive to rerun each t

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16

Peter, Mercy K., Levi Mbugua, and Anthony Wanjoya. "Bayesian Non-Parametric Mixture Model with Application to Modeling Biological Markers." Journal of Data Analysis and Information Processing 07, no. 04 (2019): 141–52. http://dx.doi.org/10.4236/jdaip.2019.74009.

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17

Bathaee, Najmeh, and Hamid Sheikhzadeh. "Non-parametric Bayesian inference for continuous density hidden Markov mixture model." Statistical Methodology 33 (December 2016): 256–75. http://dx.doi.org/10.1016/j.stamet.2016.10.003.

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18

Tanwani, Ajay Kumar, and Sylvain Calinon. "Small-variance asymptotics for non-parametric online robot learning." International Journal of Robotics Research 38, no. 1 (2018): 3–22. http://dx.doi.org/10.1177/0278364918816374.

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Small-variance asymptotics is emerging as a useful technique for inference in large-scale Bayesian non-parametric mixture models. This paper analyzes the online learning of robot manipulation tasks with Bayesian non-parametric mixture models under small-variance asymptotics. The analysis yields a scalable online sequence clustering (SOSC) algorithm that is non-parametric in the number of clusters and the subspace dimension of each cluster. SOSC groups the new datapoint in low-dimensional subspaces by online inference in a non-parametric mixture of probabilistic principal component analyzers (M

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19

Du, Xin, Yulong Pei, Wouter Duivesteijn, and Mykola Pechenizkiy. "Exceptional spatio-temporal behavior mining through Bayesian non-parametric modeling." Data Mining and Knowledge Discovery 34, no. 5 (2020): 1267–90. http://dx.doi.org/10.1007/s10618-020-00674-z.

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Abstract Collective social media provides a vast amount of geo-tagged social posts, which contain various records on spatio-temporal behavior. Modeling spatio-temporal behavior on collective social media is an important task for applications like tourism recommendation, location prediction and urban planning. Properly accomplishing this task requires a model that allows for diverse behavioral patterns on each of the three aspects: spatial location, time, and text. In this paper, we address the following question: how to find representative subgroups of social posts, for which the spatio-tempor

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20

SATO, KENGO, MICHIAKI HAMADA, TOUTAI MITUYAMA, KIYOSHI ASAI, and YASUBUMI SAKAKIBARA. "A NON-PARAMETRIC BAYESIAN APPROACH FOR PREDICTING RNA SECONDARY STRUCTURES." Journal of Bioinformatics and Computational Biology 08, no. 04 (2010): 727–42. http://dx.doi.org/10.1142/s0219720010004926.

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Since many functional RNAs form stable secondary structures which are related to their functions, RNA secondary structure prediction is a crucial problem in bioinformatics. We propose a novel model for generating RNA secondary structures based on a non-parametric Bayesian approach, called hierarchical Dirichlet processes for stochastic context-free grammars (HDP-SCFGs). Here non-parametric means that some meta-parameters, such as the number of non-terminal symbols and production rules, do not have to be fixed. Instead their distributions are inferred in order to be adapted (in the Bayesian sen

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21

Trubey, Peter, and Bruno Sansó. "Bayesian Non-Parametric Inference for Multivariate Peaks-over-Threshold Models." Entropy 26, no. 4 (2024): 335. http://dx.doi.org/10.3390/e26040335.

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We consider a constructive definition of the multivariate Pareto that factorizes the random vector into a radial component and an independent angular component. The former follows a univariate Pareto distribution, and the latter is defined on the surface of the positive orthant of the infinity norm unit hypercube. We propose a method for inferring the distribution of the angular component by identifying its support as the limit of the positive orthant of the unit p-norm spheres and introduce a projected gamma family of distributions defined through the normalization of a vector of independent

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22

Wu, Lili, Pei Shan Fam, Majid Khan Majahar Ali, Ying Tian, Mohd Tahir Ismail, and Siti Zulaikha Mohd Jamaludin. "Comparative Analysis of Improved Dirichlet Process Mixture Model." Malaysian Journal of Fundamental and Applied Sciences 19, no. 6 (2023): 1099–118. http://dx.doi.org/10.11113/mjfas.v19n6.3062.

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Due to the development of information technology, large amounts of data are generated every day in various industries such as engineering, healthcare, finance, anomaly detection, image recognition, and artificial intelligence. This massive data poses the challenge of analyzing accurately and appropriate classifications. The traditional clustering methods require specifying the number of clusters and are mostly based on distance, which cannot effectively consider the correlations between different indicators of high-dimensional and multi-source data. Moreover, the number of clusters cannot auto

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23

Zhang, Rui, Christian Walder, and Marian-Andrei Rizoiu. "Variational Inference for Sparse Gaussian Process Modulated Hawkes Process." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 6803–10. http://dx.doi.org/10.1609/aaai.v34i04.6160.

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The Hawkes process (HP) has been widely applied to modeling self-exciting events including neuron spikes, earthquakes and tweets. To avoid designing parametric triggering kernel and to be able to quantify the prediction confidence, the non-parametric Bayesian HP has been proposed. However, the inference of such models suffers from unscalability or slow convergence. In this paper, we aim to solve both problems. Specifically, first, we propose a new non-parametric Bayesian HP in which the triggering kernel is modeled as a squared sparse Gaussian process. Then, we propose a novel variational infe

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24

Nieto-Barajas, Luis E., and Fernando A. Quintana. "A Bayesian Non-Parametric Dynamic AR Model for Multiple Time Series Analysis." Journal of Time Series Analysis 37, no. 5 (2016): 675–89. http://dx.doi.org/10.1111/jtsa.12182.

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25

Albughdadi, M., L. Chaari, J. Y. Tourneret, F. Forbes, and P. Ciuciu. "A Bayesian non-parametric hidden Markov random model for hemodynamic brain parcellation." Signal Processing 135 (June 2017): 132–46. http://dx.doi.org/10.1016/j.sigpro.2017.01.005.

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26

Hou, Ying, Hai Huang, Kai Wang, and Yu Hang Zhu. "Video Call Traffic Identification Based on Bayesian Model." Advanced Materials Research 765-767 (September 2013): 1307–11. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1307.

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This paper proposes Bayesian statistical method to identify the video traffic by the symmetrical features and coding statistical characteristics of video calls. According to the problem of high computational complexity of the non-parametric probability density estimate method in the condition of large samples, we propose grid probability density estimation method of gird division to reduce the computational complexity. We present identification results. The experimental results indicate that that this method can effectively detect video call traffic.

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27

Langat, Amos Kipkorir, and John Kamwele Mutinda. "Rainfall Pattern in Kenya: Bayesian Non-parametric Model Based on the Normalized Generalized Gamma Process." Asian Journal of Probability and Statistics 26, no. 7 (2024): 34–47. http://dx.doi.org/10.9734/ajpas/2024/v26i7628.

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Understanding the pattern of rainfall in Kenya is crucial for a range of sectors, including agriculture, water management, and disaster risk reduction. In this research, we propose a Bayesian non-parametric approach to model the rainfall patterns in Kenya. Specifically, we use a hierarchical Dirichlet process mixture model to cluster the rainfall stations and identify groups of stations with similar rainfall patterns. We then model the rainfall distribution within each group using a Bayesian non-parametric model based on the normalized generalized gamma process. We apply our method to a datase

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28

Lapshin, Victor. "A nonparametric Bayesian approach to term structure fitting." Studies in Economics and Finance 36, no. 4 (2019): 600–615. http://dx.doi.org/10.1108/sef-01-2018-0025.

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Purpose This paper aims to illustrate how a Bayesian approach to yield fitting can be implemented in a non-parametric framework with automatic smoothing inferred from the data. It also briefly illustrates the advantages of such an approach using real data. Design/methodology/approach The paper uses an infinite dimensional (functional space) approach to inverse problems. Numerical computations are carried out using a Markov Chain Monte-Carlo algorithm with several tweaks to ensure good performance. The model explicitly uses bid-ask spreads to allow for observation errors and provides automatic

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29

Kamigaito, Hidetaka, Taro Watanabe, Hiroya Takamura, Manabu Okumura, and Eiichiro Sumita. "Hierarchical Back-off Modeling of Hiero Grammar based on Non-parametric Bayesian Model." Journal of Information Processing 25 (2017): 912–23. http://dx.doi.org/10.2197/ipsjjip.25.912.

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30

Johnson, Timothy D., Zhuqing Liu, Andreas J. Bartsch, and Thomas E. Nichols. "A Bayesian non-parametric Potts model with application to pre-surgical FMRI data." Statistical Methods in Medical Research 22, no. 4 (2012): 364–81. http://dx.doi.org/10.1177/0962280212448970.

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31

Zhuang, Peixian, Yue Huang, Delu Zeng, and Xinghao Ding. "Mixed noise removal based on a novel non-parametric Bayesian sparse outlier model." Neurocomputing 174 (January 2016): 858–65. http://dx.doi.org/10.1016/j.neucom.2015.09.095.

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32

Chae, Minwoo, Lizhen Lin, and David B. Dunson. "Bayesian sparse linear regression with unknown symmetric error." Information and Inference: A Journal of the IMA 8, no. 3 (2019): 621–53. http://dx.doi.org/10.1093/imaiai/iay022.

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Abstract We study Bayesian procedures for sparse linear regression when the unknown error distribution is endowed with a non-parametric prior. Specifically, we put a symmetrized Dirichlet process mixture of Gaussian prior on the error density, where the mixing distributions are compactly supported. For the prior on regression coefficients, a mixture of point masses at zero and continuous distributions is considered. Under the assumption that the model is well specified, we study behavior of the posterior with diverging number of predictors. The compatibility and restricted eigenvalue condition

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33

Ding, Xing Hao, and Xian Bo Chen. "Image Sparse Representation Based on a Nonparametric Bayesian Model." Applied Mechanics and Materials 103 (September 2011): 109–14. http://dx.doi.org/10.4028/www.scientific.net/amm.103.109.

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In recent years there has been a growing interest in the research of image sparse representation. Sparse representation based on over-complete dictionary become another hot topic in the field of image processing. In this paper a Nonparametric Bayesian model based on hierarchical Bayesian theory is proposed. In this model a sparse spike-slab prior is imposed on sparse coefficients and the Non-parametric Bayesian techniques based on sparse image representation are considering for learning dictionary. Proposed model can learn an over-complete dictionary from original image. Furthermore, the unkno

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34

Ou, Mingdong, Nan Li, Cheng Yang, Shenghuo Zhu, and Rong Jin. "Semi-Parametric Sampling for Stochastic Bandits with Many Arms." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7933–40. http://dx.doi.org/10.1609/aaai.v33i01.33017933.

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We consider the stochastic bandit problem with a large candidate arm set. In this setting, classic multi-armed bandit algorithms, which assume independence among arms and adopt non-parametric reward model, are inefficient, due to the large number of arms. By exploiting arm correlations based on a parametric reward model with arm features, contextual bandit algorithms are more efficient, but they can also suffer from large regret in practical applications, due to the reward estimation bias from mis-specified model assumption or incomplete features. In this paper, we propose a novel Bayesian fra

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35

M. Rasekhi, M. Saber, Haitham M. Yousof, and Emadeldin I. A. Ali. "Estimation of the Multicomponent Stress-Strength Reliability Model Under the Topp-Leone Distribution: Applications, Bayesian and Non-Bayesian Assessement." Statistics, Optimization & Information Computing 12, no. 1 (2023): 133–52. http://dx.doi.org/10.19139/soic-2310-5070-1685.

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The advantages of applying multicomponent stress-strength models lie in their ability to provide a comprehensive and accurate analysis of system reliability under real-world conditions. By accounting for the interactions between different stress components and identifying critical weaknesses, engineers can make informed decisions, leading to safer and more reliable designs. The primary emphasis of this research is placed on the Bayesian and classical estimations of a multicomponent stress-strength reliability model that is derived from the bounded Topp Leone distribution. It is presumable that

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36

Montano Herrera, Liliana, Tobias Eilert, I.-Ting Ho, et al. "Holistic Process Models: A Bayesian Predictive Ensemble Method for Single and Coupled Unit Operation Models." Processes 10, no. 4 (2022): 662. http://dx.doi.org/10.3390/pr10040662.

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The coupling of individual models in terms of end-to-end calculations for unit operations in manufacturing processes is a challenging task. We present a probability distribution-based approach for the combined outcomes of parametric and non-parametric models. With this so-called Bayesian predictive ensemble, the statistical moments such as mean value and standard deviation can be accurately computed without any further approximation. It is shown that the ensemble of different model predictions leads to an uninformed prior distribution, which can be transformed into a predictive posterior distr

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37

YANG, YE, CHRIS-CAROLIN SCHÖN, and DANIEL SORENSEN. "The genetics of environmental variation of dry matter grain yield in maize." Genetics Research 94, no. 3 (2012): 113–19. http://dx.doi.org/10.1017/s0016672312000304.

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SummaryDry matter grain yield per plot from three genetically homogeneous single-cross maize hybrids were analysed to investigate whether environmental variance depends on genotype. Three genotypes were tested at 20 locations in 3 years. The data were analysed using a non-parametric approach and fully parametric Bayesian models. Both analyses reveal effects of genotype on environmental variation. The Bayesian analyses indicate that genotype by location–year interactions are the most important effects acting at the level of the mean. The best-fitting Bayesian model is one postulating genotype b

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38

Härkänen, Tommi, Anna But, and Jari Haukka. "Non-parametric Bayesian Intensity Model: Exploring Time-to-Event Data on Two Time Scales." Scandinavian Journal of Statistics 44, no. 3 (2017): 798–814. http://dx.doi.org/10.1111/sjos.12280.

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39

Almeida, Marco Pollo, Rafael S. Paixão, Pedro L. Ramos, Vera Tomazella, Francisco Louzada, and Ricardo S. Ehlers. "Bayesian non-parametric frailty model for dependent competing risks in a repairable systems framework." Reliability Engineering & System Safety 204 (December 2020): 107145. http://dx.doi.org/10.1016/j.ress.2020.107145.

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40

Koutsourelakis, P. S. "A multi-resolution, non-parametric, Bayesian framework for identification of spatially-varying model parameters." Journal of Computational Physics 228, no. 17 (2009): 6184–211. http://dx.doi.org/10.1016/j.jcp.2009.05.016.

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41

Chen, Xian Bo, Xing Hao Ding, and Hui Liu. "MRI Denoising Based on a Non-Parametric Bayesian Image Sparse Representation Method." Advanced Materials Research 219-220 (March 2011): 1354–58. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.1354.

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Magnetic Resonance images are often corrupted by Gaussian noise which highly affects the quality of MR images. In this paper, a Non-Parametric hierarchical Bayesian image sparse representation method is proposed to wipe out Gaussian distribution noise coupling in MR images. In this method a spike-slab prior is imposed on sparse coefficients, and a redundant dictionary is learned from the corrupted image. Experimental results show that the method not only improves the effect of MRI denoising, but also can obtain good estimation of the noise variance. Compared to non-local filter method, this mo

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42

Niazi, Muhammad Hassan Khan, Oswaldo Morales Nápoles, and Bregje K. van Wesenbeeck. "Probabilistic Characterization of the Vegetated Hydrodynamic System Using Non-Parametric Bayesian Networks." Water 13, no. 4 (2021): 398. http://dx.doi.org/10.3390/w13040398.

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The increasing risk of flooding requires obtaining generalized knowledge for the implementation of distinct and innovative intervention strategies, such as nature-based solutions. Inclusion of ecosystems in flood risk management has proven to be an adaptive strategy that achieves multiple benefits. However, obtaining generalizable quantitative information to increase the reliability of such interventions through experiments or numerical models can be expensive, laborious, or computationally demanding. This paper presents a probabilistic model that represents interconnected elements of vegetate

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43

Stahl, Dale O. "A Bayesian Method for Characterizing Population Heterogeneity." Games 10, no. 4 (2019): 40. http://dx.doi.org/10.3390/g10040040.

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A stylized fact from laboratory experiments is that there is much heterogeneity in human behavior. We present and demonstrate a computationally practical non-parametric Bayesian method for characterizing this heterogeneity. In addition, we define the concept of behaviorally distinguishable parameter vectors, and use the Bayesian posterior to say what proportion of the population lies in meaningful regions. These methods are then demonstrated using laboratory data on lottery choices and the rank-dependent expected utility model. In contrast to other analyses, we find that 79% of the subject pop

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44

Zhu, Jun, Jianfei Chen, Wenbo Hu, and Bo Zhang. "Big Learning with Bayesian methods." National Science Review 4, no. 4 (2017): 627–51. http://dx.doi.org/10.1093/nsr/nwx044.

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AbstractThe explosive growth in data volume and the availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms, systems and applications with Big Data. Bayesian methods represent one important class of statistical methods for machine learning, with substantial recent developments on adaptive, flexible and scalable Bayesian learning. This article provides a survey of the recent advances in Big learning with Bayesian methods, termed Big Bayesian Learning, including non-parametric Bayesian meth

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Kaplan, Adam, Eric F. Lock, and Mark Fiecas. "Bayesian GWAS with Structured and Non-Local Priors." Bioinformatics 36, no. 1 (2019): 17–25. http://dx.doi.org/10.1093/bioinformatics/btz518.

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Abstract Motivation The flexibility of a Bayesian framework is promising for GWAS, but current approaches can benefit from more informative prior models. We introduce a novel Bayesian approach to GWAS, called Structured and Non-Local Priors (SNLPs) GWAS, that improves over existing methods in two important ways. First, we describe a model that allows for a marker’s gene-parent membership and other characteristics to influence its probability of association with an outcome. Second, we describe a non-local alternative model for differential minor allele rates at each marker, in which the null an

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46

Moore, C. J., A. J. K. Chua, C. P. L. Berry, and J. R. Gair. "Fast methods for training Gaussian processes on large datasets." Royal Society Open Science 3, no. 5 (2016): 160125. http://dx.doi.org/10.1098/rsos.160125.

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Gaussian process regression (GPR) is a non-parametric Bayesian technique for interpolating or fitting data. The main barrier to further uptake of this powerful tool rests in the computational costs associated with the matrices which arise when dealing with large datasets. Here, we derive some simple results which we have found useful for speeding up the learning stage in the GPR algorithm, and especially for performing Bayesian model comparison between different covariance functions. We apply our techniques to both synthetic and real data and quantify the speed-up relative to using nested samp

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47

Akanni, Wasiu A., Mark Wilkinson, Christopher J. Creevey, Peter G. Foster, and Davide Pisani. "Implementing and testing Bayesian and maximum-likelihood supertree methods in phylogenetics." Royal Society Open Science 2, no. 8 (2015): 140436. http://dx.doi.org/10.1098/rsos.140436.

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Since their advent, supertrees have been increasingly used in large-scale evolutionary studies requiring a phylogenetic framework and substantial efforts have been devoted to developing a wide variety of supertree methods (SMs). Recent advances in supertree theory have allowed the implementation of maximum likelihood (ML) and Bayesian SMs, based on using an exponential distribution to model incongruence between input trees and the supertree. Such approaches are expected to have advantages over commonly used non-parametric SMs, e.g. matrix representation with parsimony (MRP). We investigated ne

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48

Zainudin, Zulkarnain, and Sarath Kodagoda. "Gaussian Processes-BayesFilters with Non-Parametric Data Optimization for Efficient 2D LiDAR Based People Tracking." International Journal of Robotics and Control Systems 3, no. 2 (2023): 206–20. http://dx.doi.org/10.31763/ijrcs.v3i2.901.

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A model for expressing and describing human motion patterns must be able to improve tracking accuracy. However, Conventional Bayesian Filters such as Kalman Filter (KF) and Particle Filter (PF) are vulnerable to failure when dealing with highly maneuverable targets and long-term occlusions. Gaussian Processes (GP) is then used to adapt human motion patterns and integrate the model with Bayesian Filters. In GP, all samples in training phase need to be included and periodically, new samples will be added into training samples whenever it is available. Larger amount of data will increase the comp

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Koech, Ben Kiprono. "Estimation of Receiver Operating Characteristic Surface Using Mixtures of Finite Polya Trees (MFPT)." International Journal of Statistics and Probability 10, no. 2 (2021): 18. http://dx.doi.org/10.5539/ijsp.v10n2p18.

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Generalisation of Receiver operating characteristic (ROC) curve has become increasingly useful in evaluating the performance of diagnostic tests that have more than binary outcomes. While parametric approaches have been widely used over the years, the limitations associated with parametric assumptions often make it difficult to modelling the volume under surface for data that do not meet criteria under parametric distributions. As such, estimation of ROC surface using nonparametric approaches have been proposed to obtained insights on available data. One of the common approaches to non-paramet

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50

Zhai, Feifei, Jiajun Zhang, Yu Zhou, and Chengqing Zong. "Unsupervised Tree Induction for Tree-based Translation." Transactions of the Association for Computational Linguistics 1 (December 2013): 243–54. http://dx.doi.org/10.1162/tacl_a_00224.

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In current research, most tree-based translation models are built directly from parse trees. In this study, we go in another direction and build a translation model with an unsupervised tree structure derived from a novel non-parametric Bayesian model. In the model, we utilize synchronous tree substitution grammars (STSG) to capture the bilingual mapping between language pairs. To train the model efficiently, we develop a Gibbs sampler with three novel Gibbs operators. The sampler is capable of exploring the infinite space of tree structures by performing local changes on the tree nodes. Exper

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