Download or read book Nonparametric Bayesian Approaches for Acoustic Modeling written by Amir Hossein Harati Nejad Torbati and published by . This book was released on 2015 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of Bayesian analysis is to reduce the uncertainty about unobserved variables by combining prior knowledge with observations. A fundamental limitation of a parametric statistical model, including a Bayesian approach, is the inability of the model to learn new structures. The goal of the learning process is to estimate the correct values for the parameters. The accuracy of these parameters improves with more data but the model's structure remains fixed. Therefore new observations will not affect the overall complexity (e.g. number of parameters in the model). Recently, nonparametric Bayesian methods have become a popular alternative to Bayesian approaches because the model structure is learned simultaneously with the parameter distributions in a data-driven manner. The goal of this dissertation is to apply nonparametric Bayesian approaches to the acoustic modeling problem in continuous speech recognition. Three important problems are addressed: (1) statistical modeling of sub-word acoustic units; (2) semi-supervised training algorithms for nonparametric acoustic models; and (3) automatic discovery of sub-word acoustic units. We have developed a Doubly Hierarchical Dirichlet Process Hidden Markov Model (DHDPHMM) with a non-ergodic structure that can be applied to problems involving sequential modeling. DHDPHMM shares mixture components between states using two Hierarchical Dirichlet Processes (HDP). An inference algorithm for this model has been developed that enables DHDPHMM to outperform both its hidden Markov model (HMM) and HDP HMM (HDPHMM) counterparts. This inference algorithm is shown to also be computationally less expensive than a comparable algorithm for HDPHMM. In addition to sharing data, the proposed model can learn non-ergodic structures and non-emitting states, something that HDPHMM does not support. This extension to the model is used to model finite length sequences. We have also developed a generative model for semi-supervised training of DHDPHMMs. Semi-supervised learning is an important practical requirement for many machine learning applications including acoustic modeling in speech recognition. The relative improvement in error rates on classification and recognition tasks is shown to be 22% and 7% respectively. Semi-supervised training results are slightly better than supervised training (29.02% vs. 29.71%). Context modeling was also investigated and results show a modest improvement of 1.5% relative over the baseline system. We also introduce a nonparametric Bayesian transducer based on an ergodic HDPHMM/DHDPHMM that automatically segments and clusters the speech signal using an unsupervised approach. This transducer was used in several applications including speech segmentation, acoustic unit discovery, spoken term detection and automatic generation of a pronunciation lexicon. For the segmentation problem, an F¬¬¬¬¬¬-score of 76.62% was achieved which represents a 9% relative improvement over the baseline system. On the spoken term detection tasks, an average precision of 64.91% was achieved, which represents a 20% improvement over the baseline system. Lexicon generation experiments also show automatically discovered units (ADU) generalize to new datasets. In this dissertation, we have established the foundation for applications of non-parametric Bayesian modeling to problems such as speech recognition that involve sequential modeling. These models allow a new generation of machine learning systems that adapt their overall complexity in a data-driven manner and yet preserve meaningful modalities in the data. As a result, these models improve generalization and offer higher performance at lower complexity.

Download or read book A Comparative Analysis of Bayesian Nonparametric Variational Inference Algorithms for Speech Recognition written by John Steinberg and published by . This book was released on 2013 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonparametric Bayesian models have become increasingly popular in speech recognition tasks such as language and acoustic modeling due to their ability to discover underlying structure in an iterative manner. These methods do not require a priori assumptions about the structure of the data, such as the number of mixture components, and can learn this structure directly. Dirichlet process mixtures (DPMs) are a widely used nonparametric Bayesian method which can be used as priors to determine an optimal number of mixture components and their respective weights in a Gaussian mixture model (GMM). Because DPMs potentially require an infinite number of parameters, inference algorithms are needed to make posterior calculations tractable. The focus of this work is an evaluation of three of these Bayesian variational inference algorithms which have only recently become computationally viable: Accelerated Variational Dirichlet Process Mixtures (AVDPM), Collapsed Variational Stick Breaking (CVSB), and Collapsed Dirichlet Priors (CDP). To eliminate other effects on performance such as language models, a phoneme classification task is chosen to more clearly assess the viability of these algorithms for acoustic modeling. Evaluations were conducted on the CALLHOME English and Mandarin corpora, consisting of two languages that, from a human perspective, are phonologically very different. It is shown in this work that these inference algorithms yield error rates comparable to a baseline Gaussian mixture model (GMM) but with a factor of up to 20 fewer mixture components. AVDPM is shown to be the most attractive choice because it delivers the most compact models and is computationally efficient, enabling its application to big data problems.

Download or read book Bayesian Methods for Nonlinear Classification and Regression written by David G. T. Denison and published by John Wiley & Sons. This book was released on 2002-05-06 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bei der Regressionsanalyse von Datenmaterial erhält man leider selten lineare oder andere einfache Zusammenhänge (parametrische Modelle). Dieses Buch hilft Ihnen, auch komplexere, nichtparametrische Modelle zu verstehen und zu beherrschen. Stärken und Schwächen jedes einzelnen Modells werden durch die Anwendung auf Standarddatensätze demonstriert. Verbreitete nichtparametrische Modelle werden mit Hilfe von Bayes-Verfahren in einen kohärenten wahrscheinlichkeitstheoretischen Zusammenhang gebracht.

Download or read book Bayesian Speech and Language Processing written by Shinji Watanabe and published by Cambridge University Press. This book was released on 2015-07-15 with total page 447 pages. Available in PDF, EPUB and Kindle. Book excerpt: With this comprehensive guide you will learn how to apply Bayesian machine learning techniques systematically to solve various problems in speech and language processing. A range of statistical models is detailed, from hidden Markov models to Gaussian mixture models, n-gram models and latent topic models, along with applications including automatic speech recognition, speaker verification, and information retrieval. Approximate Bayesian inferences based on MAP, Evidence, Asymptotic, VB, and MCMC approximations are provided as well as full derivations of calculations, useful notations, formulas, and rules. The authors address the difficulties of straightforward applications and provide detailed examples and case studies to demonstrate how you can successfully use practical Bayesian inference methods to improve the performance of information systems. This is an invaluable resource for students, researchers, and industry practitioners working in machine learning, signal processing, and speech and language processing.

Download or read book Nonparametric Bayesian Models for Unsupervised Learning written by Pu Wang and published by . This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unsupervised learning is an important topic in machine learning. In particular, clustering is an unsupervised learning problem that arises in a variety of applications for data analysis and mining. Unfortunately, clustering is an ill-posed problem and, as such, a challenging one: no ground-truth that can be used to validate clustering results is available. Two issues arise as a consequence. Various clustering algorithms embed their own bias resulting from different optimization criteria. As a result, each algorithm may discover different patterns in a given dataset. The second issue concerns the setting of parameters. In clustering, parameter setting controls the characterization of individual clusters, and the total number of clusters in the data. Clustering ensembles have been proposed to address the issue of different biases induced by various algorithms. Clustering ensembles combine different clustering results, and can provide solutions that are robust against spurious elements in the data. Although clustering ensembles provide a significant advance, they do not address satisfactorily the model selection and the parameter tuning problem. Bayesian approaches have been applied to clustering to address the parameter tuning and model selection issues. Bayesian methods provide a principled way to address these problems by assuming prior distributions on model parameters. Prior distributions assign low probabilities to parameter values which are unlikely. Therefore they serve as regularizers for modeling parameters, and can help avoid over-fitting. In addition, the marginal likelihood is used by Bayesian approaches as the criterion for model selection. Although Bayesian methods provide a principled way to perform parameter tuning and model selection, the key question \How many clusters?" is still open. This is a fundamental question for model selection. A special kind of Bayesian methods, nonparametric Bayesian approaches, have been proposed to address this important model selection issue. Unlike parametric Bayesian models, for which the number of parameters is finite and fixed, nonparametric Bayesian models allow the number of parameters to grow with the number of observations. After observing the data, nonparametric Bayesian models t the data with finite dimensional parameters. An additional issue with clustering is high dimensionality. High-dimensional data pose a difficult challenge to the clustering process. A common scenario with high-dimensional data is that clusters may exist in different subspaces comprised of different combinations of features (dimensions). In other words, data points in a cluster may be similar to each other along a subset of dimensions, but not in all dimensions. People have proposed subspace clustering techniques, a.k.a. co-clustering or bi-clustering, to address the dimensionality issue (here, I use the term co-clustering). Like clustering, also co-clustering suffers from the ill-posed nature and the lack of ground-truth to validate the results. Although attempts have been made in the literature to address individually the major issues related to clustering, no previous work has addressed them jointly. In my dissertation I propose a unified framework that addresses all three issues at the same time. I designed a nonparametric Bayesian clustering ensemble (NBCE) approach, which assumes that multiple observed clustering results are generated from an unknown consensus clustering. The under- lying distribution is assumed to be a mixture distribution with a nonparametric Bayesian prior, i.e., a Dirichlet Process. The number of mixture components, a.k.a. the number of consensus clusters, is learned automatically. By combining the ensemble methodology and nonparametric Bayesian modeling, NBCE addresses both the ill-posed nature and the parameter setting/model selection issues of clustering. Furthermore, NBCE outperforms individual clustering methods, since it can escape local optima by combining multiple clustering results. I also designed a nonparametric Bayesian co-clustering ensemble (NBCCE) technique. NBCCE inherits the advantages of NBCE, and in addition it is effective with high dimensional data. As such, NBCCE provides a unified framework to address all the three aforementioned issues. NBCCE assumes that multiple observed co-clustering results are generated from an unknown consensus co-clustering. The underlying distribution is assumed to be a mixture with a nonparametric Bayesian prior. I developed two models to generate co-clusters in terms of row- and column- clusters. In one case row- and column-clusters are assumed to be independent, and NBCCE assumes two independent Dirichlet Process priors on the hidden consensus co-clustering, one for rows and one for columns. The second model captures the dependence between row- and column-clusters by assuming a Mondrian Process prior on the hidden consensus co-clustering. Combined with Mondrian priors, NBCCE provides more flexibility to fit the data. I have performed extensive evaluation on relational data and protein-molecule interaction data. The empirical evaluation demonstrates the effectiveness of NBCE and NBCCE and their advantages over traditional clustering and co-clustering methods.

Download or read book Robust Statistical Modeling Through Nonparametric Bayesian Methods written by Ju Hee Lee and published by . This book was released on 2010 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Nonparametric Bayesian models are commonly used to obtain robust statistical inference, and the most popular nonparametric Bayesian model is, arguably, the mixture of Dirichlet processes (MDP) model. In this study, we examine the question of how to obtain more robustness than under a conventional MDP model. In answer to this question, we develop two models from a nonparametric Bayesian viewpoint, and we investigate their properties: (i) the limiting Dirichlet process (limdir) model, and (ii) the local-mass preserving mixture of Dirichlet process (LMDP) model. The limdir model addresses the question of how to perform a ``noninformative" nonparametric Bayesian analysis. Rather than being noninformative, the model requires a slight amount of input, and so provides us with a minimally informative prior distribution with which to conduct a nonparametric Bayesian analysis. The limdir prior distribution can be viewed as the limit of a sequence of mixture of Dirichlet process models. This model requires only modest input, and yet provides posterior behavior which has a number of important qualitative features, including robustness. Second, the LMDP prior distribution focuses on local mass (defined in the paper). To specify such a prior distribution, we carefully consider the behavior of parameters of interest in some small region, and we then select a prior distribution which preserves mass in the region. Local mass preservation ties the mass of the base measure to its dispersion, resulting in robust inference. These two strategies for constructing a prior distribution can be applied to any model based on the Dirichlet process. Calibration of the prior distribution is considered. We use the limdir for the compound decision problem and the one-way analysis of variance problem, and compare its performance to that of mixture of Dirichlet processes models and to parametric Bayesian models on actual data sets. We apply the LMDP model for the one-way analysis of variance problem, and compare its performance to that of a mixture of Dirichlet processes model with a conventional prior structure. In addition to developing the robust nonparametric Bayesian models, the latter part of the study describes a general form of consistency which does not necessarily rely on correct specification of the likelihood. We carefully investigate issues of consistency and inconsistency for a variety of functions of interest, such as equality of subsets of treatment means, without the assumption that the model is correct. We prove that Bayes estimators achieve (asymptotic) consistency under some suitable regularity conditions on the assumed likelihood. More importantly, we find a need to distinguish between the notions of two parameters being "equal to one another" and "close to one another", and we illustrate differences in asymptotic inference for these two statements. This distinction carries with it implications for Bayesian tests of a point null hypothesis.

Download or read book Bayesian Approach in Acoustic Source Localization and Imaging Anglais written by Ning Chu and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Acoustic imaging is an advanced technique for acoustic source localization and power reconstruction using limited measurements at microphone sensor array. This technique can provide meaningful insights into performances, properties and mechanisms of acoustic sources. It has been widely used for evaluating the acoustic influence in automobile and aircraft industries. Acoustic imaging methods often involve in two aspects: a forward model of acoustic signal (power) propagation, and its inverse solution. However, the inversion usually causes a very ill-posed inverse problem, whose solution is not unique and is quite sensitive to measurement errors. Therefore, classical methods cannot easily obtain high spatial resolutions between two close sources, nor achieve wide dynamic range of acoustic source powers. In this thesis, we firstly build up a discrete forward model of acoustic signal propagation. This signal model is a linear but under-determined system of equations linking the measured data and unknown source signals. Based on this signal model, we set up a discrete forward model of acoustic power propagation. This power model is both linear and determined for source powers. In the forward models, we consider the measurement errors to be mainly composed of background noises at sensor array, model uncertainty caused by multi-path propagation, as well as model approximating errors. For the inverse problem of the acoustic power model, we firstly propose a robust super-resolution approach with the sparsity constraint, so that we can obtain very high spatial resolution in strong measurement errors. But the sparsity parameter should be carefully estimated for effective performance. Then for the acoustic imaging with large dynamic range and robustness, we propose a robust Bayesian inference approach with a sparsity enforcing prior: the double exponential law. This sparse prior can better embody the sparsity characteristic of source distribution than the sparsity constraint. All the unknown variables and parameters can be alternatively estimated by the Joint Maximum A Posterior (JMAP) estimation. However, this JMAP suffers a non-quadratic optimization and causes huge computational cost. So that we improve two following aspects: In order to accelerate the JMAP estimation, we investigate an invariant 2D convolution operator to approximate acoustic power propagation model. Owing to this invariant convolution model, our approaches can be parallelly implemented by the Graphics Processing Unit (GPU). Furthermore, we consider that measurement errors are spatially variant (non-stationary) at different sensors. In this more practical case, the distribution of measurement errors can be more accurately modeled by Students-t law which can express the variant variances by hidden parameters. Moreover, the sparsity enforcing distribution can be more conveniently described by the Student's-t law which can be decomposed into multivariate Gaussian and Gamma laws. However, the JMAP estimation risks to obtain so many unknown variables and hidden parameters. Therefore, we apply the Variational Bayesian Approximation (VBA) to overcome the JMAP drawbacks. One of the fabulous advantages of VBA is that it can not only achieve the parameter estimations, but also offer the confidential interval of interested parameters thanks to hidden parameters used in Students-t priors. To conclude, proposed approaches are validated by simulations, real data from wind tunnel experiments of Renault S2A, as well as the hybrid data. Compared with some typical state-of-the-art methods, the main advantages of proposed approaches are robust to measurement errors, super spatial resolutions, wide dynamic range and no need for source number nor Signal to Noise Ration (SNR) beforehand.

Download or read book Knowledge and Systems Engineering written by Van Nam Huynh and published by Springer Science & Business Media. This book was released on 2013-10-01 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of Knowledge and Systems Engineering (KSE) has experienced rapid development and inspired many applications in the world of information technology during the last decade. The KSE conference aims at providing an open international forum for presentation, discussion and exchange of the latest advances and challenges in research of the field. These proceedings contain papers presented at the Fifth International Conference on Knowledge and Systems Engineering (KSE 2013), which was held in Hanoi, Vietnam, during 17–19 October, 2013. Besides the main track of contributed papers, which are compiled into the first volume, the conference also featured several special sessions focusing on specific topics of interest as well as included one workshop, of which the papers form the second volume of these proceedings. The book gathers a total of 68 papers describing recent advances and development on various topics including knowledge discovery and data mining, natural language processing, expert systems, intelligent decision making, computational biology, computational modeling, optimization algorithms, and industrial applications.

Download or read book Modern Methodology and Applications in Spatial Temporal Modeling written by Gareth William Peters and published by Springer. This book was released on 2016-01-08 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: ​ This book provides a modern introductory tutorial on specialized methodological and applied aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter deals with non-parametric Bayesian inference via a recently developed framework known as kernel mean embedding which has had a significant influence in machine learning disciplines. The second chapter takes up non-parametric statistical methods for spatial field reconstruction and exceedance probability estimation based on Gaussian process-based models in the context of wireless sensor network data. The third chapter presents signal-processing methods applied to acoustic mood analysis based on music signal analysis. The fourth chapter covers models that are applicable to time series modeling in the domain of speech and language processing. This includes aspects of factor analysis, independent component analysis in an unsupervised learning setting. The chapter moves on to include more advanced topics on generalized latent variable topic models based on hierarchical Dirichlet processes which recently have been developed in non-parametric Bayesian literature. The final chapter discusses aspects of dependence modeling, primarily focusing on the role of extreme tail-dependence modeling, copulas, and their role in wireless communications system models.

Download or read book Computer Vision Pattern Recognition Image Processing and Graphics written by Renu Rameshan and published by Springer. This book was released on 2018-04-25 with total page 570 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 6th National Conference on Computer Vision, Pattern Recognition, Image Processing, and Graphics, NCVPRIPG 2017, held in Mandi, India, in December 2017. The 48 revised full papers presented in this volume were carefully reviewed and selected from 147 submissions. The papers are organized in topical sections on video processing; image and signal processing; segmentation, retrieval, captioning; pattern recognition applications.

Download or read book Bayesian Methods for Non gaussian Data Modeling and Applications written by Tarek Elguebaly and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Finite mixture models are among the most useful machine learning techniques and are receiving considerable attention in various applications. The use of finite mixture models in image and signal processing has proved to be of considerable interest in terms of both theoretical development and in their usefulness in several applications. In most of the applications, the Gaussian density is used in the mixture modeling of data. Although a Gaussian mixture may provide a reasonable approximation to many real-world distributions, it is certainly not always the best approximation especially in image and signal processing applications where we often deal with non-Gaussian data. In this thesis, we propose two novel approaches that may be used in modeling non-Gaussian data. These approaches use two highly flexible distributions, the generalized Gaussian distribution (GGD) and the general Beta distribution, in order to model the data. We are motivated by the fact that these distributions are able to fit many distributional shapes and then can be considered as a useful class of flexible models to address several problems and applications involving measurements and features having well-known marked deviation from the Gaussian shape. For the mixture estimation and selection problem, researchers have demonstrated that Bayesian approaches are fully optimal. The Bayesian learning allows the incorporation of prior knowledge in a formal coherent way that avoids overfitting problems. For this reason, we adopt different Bayesian approaches in order to learn our models parameters. First, we present a fully Bayesian approach to analyze finite generalized Gaussian mixture models which incorporate several standard mixtures, such as Laplace and Gaussian. This approach evaluates the posterior distribution and Bayes estimators using a Gibbs sampling algorithm, and selects the number of components in the mixture using the integrated likelihood. We also propose a fully Bayesian approach for finite Beta mixtures learning using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) technique which simultaneously allows cluster assignments, parameters estimation, and the selection of the optimal number of clusters. We then validate the proposed methods by applying them to different image processing applications.

Download or read book Computer Vision ECCV 2018 written by Vittorio Ferrari and published by Springer. This book was released on 2018-10-05 with total page 757 pages. Available in PDF, EPUB and Kindle. Book excerpt: The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.

Download or read book Nonparametric Bayesian Methods for Supervised and Unsupervised Learning written by Vikash Kumar Mansinghka and published by . This book was released on 2009 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: I introduce two nonparametric Bayesian methods for solving problems of supervised and unsupervised learning. The first method simultaneously learns causal networks and causal theories from data. For example, given synthetic co-occurrence data from a simple causal model for the medical domain, it can learn relationships like "having a flu causes coughing", while also learning that observable quantities can be usefully grouped into categories like diseases and symptoms, and that diseases tend to cause symptoms, not the other way around. The second method is an online algorithm for learning a prototype-based model for categorial concepts, and can be used to solve problems of multiclass classification with missing features. I apply it to problems of categorizing newsgroup posts and recognizing handwritten digits. These approaches were inspired by a striking capacity of human learning, which should also be a desideratum for any intelligent system: the ability to learn certain kinds of "simple" or "natural" structures very quickly, while still being able to learn arbitrary -- and arbitrarily complex - structures given enough data. In each case, I show how nonparametric Bayesian modeling and inference based on stochastic simulation give us some of the tools we need to achieve this goal.

Download or read book Some Advances in Bayesian Nonparametric Modeling written by Abel Rodriguez and published by LAP Lambert Academic Publishing. This book was released on 2009-03 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian nonparametric and semiparametric mixture models have become extremely popular in the last 10 years because they provide flexibility and interpretability while preserving computational simplicity. This book is a contribution to this growing literature, discussing the design of models for collections of distributions and their application to density estimation and nonparametric regression. All methods introduced in this book are discussed in the context of complex scientific applications in public health, epidemiology and finance.

Download or read book Bayesian Nonparametrics written by J.K. Ghosh and published by Springer Science & Business Media. This book was released on 2006-05-11 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. It will also appeal to statisticians in general. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian non-parametrics.

Download or read book Bayesian Analysis in Natural Language Processing written by Shay Cohen and published by Springer Nature. This book was released on 2022-11-10 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Natural language processing (NLP) went through a profound transformation in the mid-1980s when it shifted to make heavy use of corpora and data-driven techniques to analyze language. Since then, the use of statistical techniques in NLP has evolved in several ways. One such example of evolution took place in the late 1990s or early 2000s, when full-fledged Bayesian machinery was introduced to NLP. This Bayesian approach to NLP has come to accommodate for various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction examples. We cover the methods and algorithms that are needed to fluently read Bayesian learning papers in NLP and to do research in the area. These methods and algorithms are partially borrowed from both machine learning and statistics and are partially developed "in-house" in NLP. We cover inference techniques such as Markov chain Monte Carlo sampling and variational inference, Bayesian estimation, and nonparametric modeling. We also cover fundamental concepts in Bayesian statistics such as prior distributions, conjugacy, and generative modeling. Finally, we cover some of the fundamental modeling techniques in NLP, such as grammar modeling and their use with Bayesian analysis.

Download or read book Probabilistic Bayesian Approaches to Model the Global Vibro acoustic Performance of Vehicles written by Gianluigi Brogna and published by . This book was released on 2019 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the automotive domain, although already quite elaborate, the current approaches to predict and analyse the vibro-acoustic behaviour of a vehicle are still far from the complexity of the real system. Among other limitations, design specifications are still essentially based on extreme loading conditions, useful when verifying the mechanical strength, but not representative of the actual vehicle usage, which is instead important when addressing the vibro-acoustic performance. As a consequence, one main aim here is to build a prediction model able to take into account the loading scenarios representative of the actual vehicle usage, as well as the car structural uncertainty (due, for instance, to production dispersion). The proposed model shall cover the low and mid-frequency domain. To this aim, four main steps are proposed in this work: (1) the definition of a model for a general vehicle system, pertinent to the vibro-acoustic responses of interest; (2) the estimation of the whole set of loads applied to this system in a large range of operating conditions; (3) the statistical analysis and modelling of these loads as a function of the vehicle operating conditions; (4) the analysis of the application of the modelled loads to non-parametric stochastic transfer functions, representative of the vehicle structural uncertainty. To achieve the previous steps, ad hoc Bayesian algorithms have been developed and applied to a large industrial database. The Bayesian framework is considered here particularly valuable since it allows taking into account prior knowledge, namely from automotive experts, and since it easily enables uncertainty propagation between the layers of the probabilistic model. Finally, this work shows that the proposed algorithms, more than simply yielding a model of the vibro-acoustic response of a vehicle, are also useful to gain deep insights on the dominant physical mechanisms at the origin of the response of interest.