Journal articles: 'Bayesian non-Parametric model'

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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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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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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 (July 31, 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 maximum likelihood method for parameter estimation in the binary logistic regression model.

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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 (August 12, 2021): 2104–23. http://dx.doi.org/10.1080/10807039.2021.1956298.

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

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Xia, Yunqing. "Application of non parametric Bayesian methods in high dimensional data." Journal of Computational Methods in Sciences and Engineering 24, no. 2 (May 10, 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 combine non parametric methods with Bayesian methods for application research in high-dimensional data analysis. In this paper, the nonparametric Bayesian method was applied to the analysis of high-dimensional data, and the Dirichlet process Mixture model was used to cluster high-dimensional data. The regression analysis of high-dimensional data was carried out through the prediction model of nonparametric Bayesian regression. In this paper, the nonparametric Bayesian method based on Bayesian sparse linear model was used for feature selection of high-dimensional data. In order to determine the superiority of nonparametric Bayesian methods in high-dimensional data analysis, this paper conducted experiments on nonparametric Bayesian methods and traditional parametric methods in high-dimensional data analysis from five aspects of cluster analysis, classification analysis, regression analysis, feature selection and anomaly detection, and evaluated them through multiple indicators. This article explored the application of non parametric Bayesian methods in high-dimensional data analysis from these aspects through simulation experiments. The experimental results show that the clustering accuracy of the non parametric Bayesian clustering algorithm was 0.93, and the accuracy of the non parametric Bayesian classification algorithm was between 0.93 and 0.99; the coefficient of determination of nonparametric Bayesian regression algorithm was 0.98; the F1 values of non parametric Bayesian methods in anomaly detection ranged from 0.86 to 0.91, which was superior to traditional methods. Non parametric Bayesian methods have broad application prospects in high-dimensional data analysis, and can be applied in multiple fields such as clustering, classification, regression, etc.

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Li, Hong, and Yang Lu. "A Bayesian non-parametric model for small population mortality." Scandinavian Actuarial Journal 2018, no. 7 (January 2, 2018): 605–28. http://dx.doi.org/10.1080/03461238.2017.1418420.

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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 (July 9, 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 insurance setting. In the parametric framework, quantile functions are derived using several distributions including the flexible generalized beta (GB2) distribution family, asymmetric Laplace (AL) distribution and power-Pareto (PP) distribution. In these parametric model based quantile regressions, we detail two basic formulations. The first involves embedding the quantile regression loss function from the nonparameteric setting into the argument of the kernel of a parametric data likelihood model, this is well known to naturally lead to the AL parametric model case. The second formulation we utilize in the parametric setting adopts an alternative quantile regression formulation in which we assume a structural expression for the regression trend and volatility functions which act to modify a base quantile function in order to produce the conditional data quantile function. This second approach allows a range of flexible parametric models to be considered with different tail behaviors. We demonstrate how to perform estimation of the resulting parametric models under a Bayesian regression framework. To achieve this, we design Markov chain Monte Carlo (MCMC) sampling strategies for the resulting Bayesian posterior quantile regression models. In the non-parametric framework, we construct quantile functions by minimizing an asymmetrically weighted loss function and estimate the parameters under the AL proxy distribution to resemble the minimization process. This quantile regression model is contrasted to the parametric AL mean regression model and both are expressed as a scale mixture of uniform distributions to facilitate efficient implementation. The models are extended to adopt dynamic mean, variance and skewness and applied to analyze two real loss reserve data sets to perform inference and discuss interesting features of quantile regression for risk margin calculations.

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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 (August 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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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 (November 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 such as containing strict restrictions in its parametric component, complete flexibility in its non-parametric component, and clarity of the interaction between its parametric and non-parametric components. According to the above, two methods were used to estimate a semi-parametric balanced longitudinal data model. The first is the Bayesian estimating method; the second is the Speckman method, which estimated the unknown nonparametric smoothing function by employing the kernel smoothing Nadaraya & Watson method. The Aim was to make a comparison between the Bayesian estimation method and the classical estimation method. Based on simulation experiments conducted on three different sample sizes (50, 100, and 200), it was concluded that the Bayes method is best at the variance levels (1,5). In contrast, the Profile least square method is best at the variance level (10).

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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 (February 12, 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 specifications, weighting the contribution of each model via Bayesian model averaging (BMA). Simulation studies show that the non-parametric modeling approach with BMA provides robust sensitivity and specificity for detecting hormesis relative to the parametric approach, regardless of the shape of a hormetic zone.

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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 (October 26, 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 for such confounding effects in population growth data is not currently available. Further, our previous work has demonstrated that parametric models are insufficient to explain and predict microbial response under non-standard growth conditions. Here we develop a hierarchical Bayesian non-parametric model of population growth that identifies the latent growth behavior and response to perturbation, while simultaneously correcting for random effects in the data. This model enables more accurate estimates of the biological effect of interest, while better accounting for the uncertainty due to technical variation. Additionally, modeling hierarchical variation provides estimates of the relative impact of various confounding effects on measured population growth.

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Hong, Liang, and Ryan Martin. "Real-time Bayesian non-parametric prediction of solvency risk." Annals of Actuarial Science 13, no. 1 (February 7, 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 time new data arrives. In this paper, we apply a recently developed alternative perspective on Bayesian prediction based on copulas. This approach facilitates recursive Bayesian prediction without computing a posterior, allowing insurers to perform real-time updating of risk measures to assess solvency risk, and providing them with a tool for carrying out dynamic risk management strategies in today’s “big data” era.

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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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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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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 (October 25, 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 non-parametric methods for forecasting non-linear and non-stationary time series are considered. Parametric methods include methods: classical autoregressive models, neural networks, models of support vector machines, hidden Markov models. Non-parametric methods include methods: state-space models, functional decomposition models, Bayesian non-parametric models. One of the types of non-parametric models isBayesian structural time series. The main features of constructing structural time series are considered. Models of structural time series are presented. The process of learning the Bayesianstructural model of time series is described. Training is performed in four stages: setting the structure of the model and a priori probabilities; applying a Kalman filter to update state estimates based on observed data;application of the “spike-and-slab”method to select variables in a structural model; Bayesian averaging to combine the results to make a prediction. An algorithm for constructing a Bayesian structural time seriesmodel is presented. Various components of the BSTS model are considered andanalysed, with the help of which the structures of alternative predictive models are formed. As an example of the application of Bayesian structural time series, the problem of predicting Amazon stock prices is considered. The base dataset is amzn_share. After loading, the structure and data types were analysed, and missing values were processed. The data are characterized by irregular registration of observations, which leads to a large number of missing values and “masking” possible seasonal fluctuations. This makes the task of forecasting rather difficult. To restore gaps in the amzn_sharetime series, the linear interpolation method was used. Using a set of statistical tests (ADF, KPSS, PP), the series was tested for stationarity. The data set is divided into two parts: training and testing. The fitting of structural models of time series was performed using the Kalman filterand the Monte Carlo method according to the Markov chain scheme. To estimate and simultaneously regularize the regression coefficients, the spike-and-slab method was applied. The quality of predictive models was assessed.

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Tanwani, Ajay Kumar, and Sylvain Calinon. "Small-variance asymptotics for non-parametric online robot learning." International Journal of Robotics Research 38, no. 1 (December 11, 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 (MPPCA) based on a Dirichlet process, and captures the state transition and state duration information online in a hidden semi-Markov model (HSMM) based on a hierarchical Dirichlet process. A task-parameterized formulation of our approach autonomously adapts the model to changing environmental situations during manipulation. We apply the algorithm in a teleoperation setting to recognize the intention of the operator and remotely adjust the movement of the robot using the learned model. The generative model is used to synthesize both time-independent and time-dependent behaviors by relying on the principles of shared and autonomous control. Experiments with the Baxter robot yield parsimonious clusters that adapt online with new demonstrations and assist the operator in performing remote manipulation tasks.

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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 (January 29, 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-temporal behavioral patterns are substantially different from the behavioral patterns in the whole dataset? Selection and evaluation are the two challenging problems for finding the exceptional subgroups. To address these problems, we propose BNPM: a Bayesian non-parametric model, to model spatio-temporal behavior and infer the exceptionality of social posts in subgroups. By training BNPM on a large amount of randomly sampled subgroups, we can get the global distribution of behavioral patterns. For each given subgroup of social posts, its posterior distribution can be inferred by BNPM. By comparing the posterior distribution with the global distribution, we can quantify the exceptionality of each given subgroup. The exceptionality scores are used to guide the search process within the exceptional model mining framework to automatically discover the exceptional subgroups. Various experiments are conducted to evaluate the effectiveness and efficiency of our method. On four real-world datasets our method discovers subgroups coinciding with events, subgroups distinguishing professionals from tourists, and subgroups whose consistent exceptionality can only be truly appreciated by combining exceptional spatio-temporal and exceptional textual behavior.

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Trubey, Peter, and Bruno Sansó. "Bayesian Non-Parametric Inference for Multivariate Peaks-over-Threshold Models." Entropy 26, no. 4 (April 14, 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 random gammas to the space. This serves to construct a flexible family of distributions obtained as a Dirichlet process mixture of projected gammas. For model assessment, we discuss scoring methods appropriate to distributions on the unit hypercube. In particular, working with the energy score criterion, we develop a kernel metric that produces a proper scoring rule and presents a simulation study to compare different modeling choices using the proposed metric. Using our approach, we describe the dependence structure of extreme values in the integrated vapor transport (IVT), data describing the flow of atmospheric moisture along the coast of California. We find clear but heterogeneous geographical dependence.

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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 (August 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 sense) to the training sequences provided. The results of our RNA secondary structure predictions show that HDP-SCFGs are more accurate than the MFE-based and other generative models.

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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 (February 8, 2016): 675–89. http://dx.doi.org/10.1111/jtsa.12182.

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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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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 (December 4, 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 automatically adjust when new data is generated. In order to improve the clustering analysis of high-dimensional and multi-source data in a big data environment, this study utilizes non-parametric mixture models based on distribution clustering, which does not require specifying the number of clusters and can auto update with the data. By combining Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and the non-parametric Bayesian method called Dirichlet Process Mixture Model (DPMM), the Bayesian non-parametric PCA model (PCA-DPMM) and Bayesian non-parametric t-SNE model (TSNE-DPMM) are proposed. The Chinese restaurant process of DPMM is used for sampling by introducing a finite normal mixture distribution. The clustering results on the iris dataset are compared and analyzed. The accuracy of DPMM and TSNE-DPMM reaches 0.97, while PCA-DPMM achieves a maximum accuracy of only 0.94. When different numbers of iterations are set, TSNE-DPMM maintains an accuracy ranging from 0.92 to 0.97, DPMM ranges from 0.66 to 0.97, and PCA-DPMM ranges from 0.73 to 0.94. Therefore, the proposed TSNE-DPMM ensures accuracy and exhibits better model stability in clustering results. Future research can explore the improvement of the model by incorporating deep learning algorithms, among others, to further enhance its performance. Additionally, applying the TSNE-DPMM model to data analysis in other fields is also a future research direction. Through these efforts, we can better tackle the challenges of analyzing high-dimensional and multi-source data in a big data environment and extract valuable information from it.

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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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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 (April 3, 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 inference schema for model optimization. We employ the branching structure of the HP so that maximization of evidence lower bound (ELBO) is tractable by the expectation-maximization algorithm. We propose a tighter ELBO which improves the fitting performance. Further, we accelerate the novel variational inference schema to linear time complexity by leveraging the stationarity of the triggering kernel. Different from prior acceleration methods, ours enjoys higher efficiency. Finally, we exploit synthetic data and two large social media datasets to evaluate our method. We show that our approach outperforms state-of-the-art non-parametric frequentist and Bayesian methods. We validate the efficiency of our accelerated variational inference schema and practical utility of our tighter ELBO for model selection. We observe that the tighter ELBO exceeds the common one in model selection.

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Lapshin, Victor. "A nonparametric Bayesian approach to term structure fitting." Studies in Economics and Finance 36, no. 4 (October 7, 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 smoothing based on them. Findings A non-parametric framework allows to capture complex shapes of zero-coupon yield curves typical for emerging markets. Bayesian approach allows to assess the precision of estimates, which is crucial for some applications. Examples of estimation results are reported for three different bond markets: liquid (German), medium liquidity (Chinese) and illiquid (Russian). Practical implications The result shows that infinite-dimensional Bayesian approach to term structure estimation is feasible. Market practitioners could use this approach to gain more insight into interest rates term structure. For example, they could now be able to complement their non-parametric term structure estimates with Bayesian confidence intervals, which would allow them to assess statistical significance of their results. Originality/value The model does not require parameter tuning during estimation. It has its own parameters, but they are to be selected during model setup.

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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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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 (May 23, 2012): 364–81. http://dx.doi.org/10.1177/0962280212448970.

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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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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 (January 9, 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 conditions yield the minimax convergence rate of the regression coefficients in $\ell _1$- and $\ell _2$-norms, respectively. In addition, strong model selection consistency and a semi-parametric Bernstein–von Mises theorem are proven under slightly stronger conditions.

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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 (November 13, 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 both stress and strength follow a Topp Leone distribution, but the shape parameters of each variable differ, and the scale parameters (which determine where the variable is bounded) remain the same. Statisticians utilize approaches such as maximum likelihood paired with parametric and non-parametric bootstrap, as well as Bayesian methods, in order to evaluate the dependability of a system. Bayesian methods are also utilized. Simulation studies are carried out with the intention of establishing the degree of precision that may be achieved by employing the various methods of estimating. For the sake of this example, two genuine data sets are dissected and examined in detail.

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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 unknown noise variance can be estimated from noisy image. As regards to the image sparse representation, proposed model obtains good sparse solution. Comparing to other state-of-the-art image sparse representation method, this model obtains better reconstruction effects.

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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 framework, called Semi-Parametric Sampling (SPS), for this problem, which employs semi-parametric function as the reward model. Specifically, the parametric part of SPS, which models expected reward as a parametric function of arm feature, can efficiently eliminate poor arms from candidate set. The non-parametric part of SPS, which adopts nonparametric reward model, revises the parametric estimation to avoid estimation bias, especially on the remained candidate arms. We give an implementation of SPS, Linear SPS (LSPS), which utilizes linear function as the parametric part. In semi-parametric environment, theoretical analysis shows that LSPS achieves better regret bound (i.e. O̴(√N1−α dα √T) with α ∈ [0, 1])) than existing approaches. Also, experiments demonstrate the superiority of the proposed approach.

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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 (June 23, 2017): 798–814. http://dx.doi.org/10.1111/sjos.12280.

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

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Montano Herrera, Liliana, Tobias Eilert, I.-Ting Ho, Milena Matysik, Michael Laussegger, Ralph Guderlei, Bernhard Schrantz, Alexander Jung, Erich Bluhmki, and Jens Smiatek. "Holistic Process Models: A Bayesian Predictive Ensemble Method for Single and Coupled Unit Operation Models." Processes 10, no. 4 (March 29, 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 distribution using Bayesian inference and numerical Markov Chain Monte Carlo calculations. We demonstrate the advantages of our method using several numerical examples. Our approach is not restricted to certain unit operations, and can also be used for the more robust interpretation and assessment of model predictions in general.

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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 model shows better visual quality as well as higher PSNR.

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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 (May 28, 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 by location–year interactions acting on the mean and main effects of genotype and of location–year on the variance. Despite the detection of genotypic effects acting on the variance, location–year effects constitute the biggest relative source of variance heterogeneity.

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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 (February 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 vegetated hydrodynamic systems using a nonparametric Bayesian network (NPBN) for seagrasses, salt marshes, and mangroves. NPBNs allow for a system-level probabilistic description of vegetated hydrodynamic systems, generate physically realistic varied boundary conditions for physical or numerical modeling, provide missing information in data-scarce environments, and reduce the amount of numerical simulations required to obtain generalized results—all of which are critically useful to pave the way for successful implementation of nature-based solutions.

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Stahl, Dale O. "A Bayesian Method for Characterizing Population Heterogeneity." Games 10, no. 4 (October 9, 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 population is not behaviorally distinguishable from the ordinary expected utility model.

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Kaplan, Adam, Eric F. Lock, and Mark Fiecas. "Bayesian GWAS with Structured and Non-Local Priors." Bioinformatics 36, no. 1 (June 22, 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 and alternative hypotheses have no common support. Results We employ a non-parametric model that allows for clustering of the genes in tandem with a regression model for marker-level covariates, and demonstrate how incorporating these additional characteristics can improve power. We further demonstrate that our non-local alternative model gives symmetric rates of convergence for the null and alternative hypotheses, whereas commonly used local alternative models have asymptotic rates that favor the alternative hypothesis over the null. We demonstrate the robustness and flexibility of our structured and non-local model for different data generating scenarios and signal-to-noise ratios. We apply our Bayesian GWAS method to single nucleotide polymorphisms data collected from a pool of Alzheimer’s disease and cognitively normal patients from the Alzheimer’s Database Neuroimaging Initiative. Availability and implementation R code to perform the SNLPs method is available at https://github.com/lockEF/BayesianScreening.

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Zhu, Jun, Jianfei Chen, Wenbo Hu, and Bo Zhang. "Big Learning with Bayesian methods." National Science Review 4, no. 4 (May 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 methods for adaptively inferring model complexity, regularized Bayesian inference for improving the flexibility via posterior regularization, and scalable algorithms and systems based on stochastic subsampling and distributed computing for dealing with large-scale applications. We also provide various new perspectives on the large-scale Bayesian modeling and inference.

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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 (May 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 sampling to numerically evaluate model evidences.

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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 (March 19, 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 computational time to produce the learned GP models due to data redundancies. As a result, Mutual Information (MI) based technique with Mahalanobis Distance (MD) is developed to keep only the informative data. This method is used to process data which is collected by a robot equipped with a LiDAR. Experiments have demonstrated that reducing data does not raise Average Root Mean Square Error (ARMSE) considerably. EKF, PF, GP-EKF and GP-PF are utilised as a tool for tracking people and all techniques have been analyzed in order to distinguish which method is more efficient. The performance of GP-EKF and GP-PF are then compared to EKF and PF where it proved that GP-BayesFilters performs better than Conventional Bayesian Filters. The proposed approach has reduced data points up to more than 90\% while keeping the ARMSE within acceptable limits. This data optimization technique will save computational time especially when deal with periodically accumulative data sets. Comparing on four tracking methods, both GP-PF and GP-EKF have achieved higher tracking performance when dealing with highly maneuverable targets and occlusions.

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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 (August 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 new implementations of ML and Bayesian SMs and compared these with some currently available alternative approaches. Comparisons include hypothetical examples previously used to investigate biases of SMs with respect to input tree shape and size, and empirical studies based either on trees harvested from the literature or on trees inferred from phylogenomic scale data. Our results provide no evidence of size or shape biases and demonstrate that the Bayesian method is a viable alternative to MRP and other non-parametric methods. Computation of input tree likelihoods allows the adoption of standard tests of tree topologies (e.g. the approximately unbiased test). The Bayesian approach is particularly useful in providing support values for supertree clades in the form of posterior probabilities.

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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 (January 25, 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-parametric estimation is the use of Bayesian models where assumptions about priors can be made then posterior distributions obtained which can then be used to model the data. This study uses Polya tree priors where mixtures of Polya trees approach was used to model simulated three-way ROC data. The results of VUS estimation which is considered a suitable inference in evaluating performance of a diagnostic test, indicated that the mixtures of Polya trees model fitted well the ROC surface data. Further, the model performed relatively well compared to parametric and semiparametric models under similar assumptions.  

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Virbickaitė, Audronė, M. Concepción Ausín, and Pedro Galeano. "A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection." Computational Statistics & Data Analysis 100 (August 2016): 814–29. http://dx.doi.org/10.1016/j.csda.2014.12.005.

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Zhao, Bonan, Christopher G. Lucas, and Neil R. Bramley. "How Do People Generalize Causal Relations over Objects? A Non-parametric Bayesian Account." Computational Brain & Behavior 5, no. 1 (November 30, 2021): 22–44. http://dx.doi.org/10.1007/s42113-021-00124-z.

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AbstractHow do people decide how general a causal relationship is, in terms of the entities or situations it applies to? What features do people use to decide whether a new situation is governed by a new causal law or an old one? How can people make these difficult judgments in a fast, efficient way? We address these questions in two experiments that ask participants to generalize from one (Experiment 1) or several (Experiment 2) causal interactions between pairs of objects. In each case, participants see an agent object act on a recipient object, causing some changes to the recipient. In line with the human capacity for few-shot concept learning, we find systematic patterns of causal generalizations favoring simpler causal laws that extend over categories of similar objects. In Experiment 1, we find that participants’ inferences are shaped by the order of the generalization questions they are asked. In both experiments, we find an asymmetry in the formation of causal categories: participants preferentially identify causal laws with features of the agent objects rather than recipients. To explain this, we develop a computational model that combines program induction (about the hidden causal laws) with non-parametric category inference (about their domains of influence). We demonstrate that our modeling approach can both explain the order effect in Experiment 1 and the causal asymmetry, and outperforms a naïve Bayesian account while providing a computationally plausible mechanism for real-world causal generalization.

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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. Experimental results show that the string-to-tree translation system using our Bayesian tree structures significantly outperforms the strong baseline string-to-tree system using parse trees.

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Journal articles on the topic 'Bayesian non-Parametric model'

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