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Patterns Of Scalable Bayesian Inference
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Book Synopsis Patterns of Scalable Bayesian Inference by : Elaine Angelino
Download or read book Patterns of Scalable Bayesian Inference written by Elaine Angelino and published by . This book was released on 2016-11-17 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Identifies unifying principles, patterns, and intuitions for scaling Bayesian inference. Reviews existing work on utilizing modern computing resources with both MCMC and variational approximation techniques. From this taxonomy of ideas, it characterizes the general principles that have proven successful for designing scalable inference procedures.
Book Synopsis Scalable Bayesian Inference for Generalized Multivariate Dynamic Linear Models by : Manan Saxena
Download or read book Scalable Bayesian Inference for Generalized Multivariate Dynamic Linear Models written by Manan Saxena and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generalized Multivariate Dynamic Linear Models (GMDLMs) are a flexible class of multivariate time series models well-suited for non-Gaussian observations. They represent a special case within the more widely recognized multinomial logistic-normal (MLN) models. They are effective for analyzing sequence count data due to their ability to handle complex covariance structures and provide interpretability/control over the structure of the model. However, their current implementations are limited to small datasets, primarily because of computational inefficiency and increased variance in parameter estimates. Our work addresses the need for scalable Bayesian inference methods for these models. We develop an efficient method for obtaining a point estimate of our parameter by using the Kalman Filter and calculating closed-form gradients for our optimizer. Additionally, we provide uncertainty quantification of our parameter using Multinomial Dirichlet Bootstrap and refine these estimates further with Particle Refinement. We demonstrate that our inference scheme is considerably faster than STAN and provides a reliable approximation comparable to results obtained from MCMC.
Book Synopsis Scalable Bayesian spatial analysis with Gaussian Markov random fields by : Per Sidén
Download or read book Scalable Bayesian spatial analysis with Gaussian Markov random fields written by Per Sidén and published by Linköping University Electronic Press. This book was released on 2020-08-17 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate statistical analysis of spatial data is important in many applications. Failing to properly account for spatial autocorrelation may often lead to false conclusions. At the same time, the ever-increasing sizes of spatial datasets pose a great computational challenge, as many standard methods for spatial analysis are limited to a few thousand data points. In this thesis, we explore how Gaussian Markov random fields (GMRFs) can be used for scalable analysis of spatial data. GMRFs are closely connected to the commonly used Gaussian processes, but have sparsity properties that make them computationally cheap both in time and memory. The Bayesian framework enables a GMRF to be used as a spatial prior, comprising the assumption of smooth variation over space, and gives a principled way to estimate the parameters and propagate uncertainty. We develop new algorithms that enable applying GMRF priors in 3D to the brain activity inherent in functional magnetic resonance imaging (fMRI) data, with millions of observations. We show that our methods are both faster and more accurate than previous work. A method for approximating selected elements of the inverse precision matrix (i.e. the covariance matrix) is also proposed, which is important for evaluating the posterior uncertainty. In addition, we establish a link between GMRFs and deep convolutional neural networks, which have been successfully used in countless machine learning tasks for images, resulting in a deep GMRF model. Finally, we show how GMRFs can be used in real-time robotic search and rescue operations, for modeling the spatial distribution of injured persons. Tillförlitlig statistisk analys av spatiala data är viktigt inom många tillämpningar. Om inte korrekt hänsyn tas till spatial autokorrelation kan det ofta leda till felaktiga slutsatser. Samtidigt ökar ständigt storleken på de spatiala datamaterialen vilket utgör en stor beräkningsmässig utmaning, eftersom många standardmetoder för spatial analys är begränsade till några tusental datapunkter. I denna avhandling utforskar vi hur Gaussiska Markov-fält (eng: Gaussian Markov random fields, GMRF) kan användas för mer skalbara analyser av spatiala data. GMRF-modeller är nära besläktade med de ofta använda Gaussiska processerna, men har gleshetsegenskaper som gör dem beräkningsmässigt effektiva både vad gäller tids- och minnesåtgång. Det Bayesianska synsättet gör det möjligt att använda GMRF som en spatial prior som innefattar antagandet om långsam spatial variation och ger ett principiellt tillvägagångssätt för att skatta parametrar och propagera osäkerhet. Vi utvecklar nya algoritmer som gör det möjligt att använda GMRF-priors i 3D för den hjärnaktivitet som indirekt kan observeras i hjärnbilder framtagna med tekniken fMRI, som innehåller milliontals datapunkter. Vi visar att våra metoder är både snabbare och mer korrekta än tidigare forskning. En metod för att approximera utvalda element i den inversa precisionsmatrisen (dvs. kovariansmatrisen) framförs också, vilket är viktigt för att kunna evaluera osäkerheten i posteriorn. Vidare gör vi en koppling mellan GMRF och djupa neurala faltningsnätverk, som har använts framgångsrikt för mängder av bildrelaterade problem inom maskininlärning, vilket mynnar ut i en djup GMRF-modell. Slutligen visar vi hur GMRF kan användas i realtid av autonoma drönare för räddningsinsatser i katastrofområden för att modellera den spatiala fördelningen av skadade personer.
Book Synopsis Scalable Bayesian Inference for Stochastic Epidemic Processes by : Martin Burke
Download or read book Scalable Bayesian Inference for Stochastic Epidemic Processes written by Martin Burke and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Practical Methods for Scalable Bayesian and Causal Inference with Provable Quality Guarantees by : Raj Agrawal (Computer scientist)
Download or read book Practical Methods for Scalable Bayesian and Causal Inference with Provable Quality Guarantees written by Raj Agrawal (Computer scientist) and published by . This book was released on 2021 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many scientific and decision-making tasks require learning complex relationships between a set of p covariates and a target response, from N observed datapoints with N “p. For example, in genomics and precision medicine, there may be thousands or millions of genetic and environmental covariates but just hundreds or thousands of observed individuals. Researchers would like to (1) identify a small set of factors associated with diseases, (2) quantify these factors' effects, and (3) test for causality. Unfortunately, in this high-dimensional data regime, inference is statistically and computationally challenging due to non-linear interaction effects, unobserved confounders, and the lack of randomized experimental data. In this thesis, I start by addressing the problems of variable selection and estimation when there are non-linear interactions and fewer datapoints than covariates. Unlike previous methods whose runtimes scale at least quadratically in the number of covariates, my new method (SKIM-FA) uses a kernel trick to perform inference in linear time by exploiting special interaction structure. While SKIM-FA identifies potential risk-factors, not all of these factors need be causal. So next I aim to identify causal factors to aid in decision making. To this end, I show when we can extract causal relationships from observational data, even in the presence of unobserved confounders, non-linear effects, and a lack of randomized controlled data. In the last part of my thesis, I focus on experimental design. Specifically, if the observational data is not adequate, how do we optimally collect new experimental data to test if particular causal relationships of interest exist.
Book Synopsis Scaling Bayesian Inference by : Jonathan Hunter Huggins
Download or read book Scaling Bayesian Inference written by Jonathan Hunter Huggins and published by . This book was released on 2018 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian statistical modeling and inference allow scientists, engineers, and companies to learn from data while incorporating prior knowledge, sharing power across experiments via hierarchical models, quantifying their uncertainty about what they have learned, and making predictions about an uncertain future. While Bayesian inference is conceptually straightforward, in practice calculating expectations with respect to the posterior can rarely be done in closed form. Hence, users of Bayesian models must turn to approximate inference methods. But modern statistical applications create many challenges: the latent parameter is often high-dimensional, the models can be complex, and there are large amounts of data that may only be available as a stream or distributed across many computers. Existing algorithm have so far remained unsatisfactory because they either (1) fail to scale to large data sets, (2) provide limited approximation quality, or (3) fail to provide guarantees on the quality of inference. To simultaneously overcome these three possible limitations, I leverage the critical insight that in the large-scale setting, much of the data is redundant. Therefore, it is possible to compress data into a form that admits more efficient inference. I develop two approaches to compressing data for improved scalability. The first is to construct a coreset: a small, weighted subset of our data that is representative of the complete dataset. The second, which I call PASS-GLM, is to construct an exponential family model that approximates the original model. The data is compressed by calculating the finite-dimensional sufficient statistics of the data under the exponential family. An advantage of the compression approach to approximate inference is that an approximate likelihood substitutes for the original likelihood. I show how such approximate likelihoods lend them themselves to a priori analysis and develop general tools for proving when an approximate likelihood will lead to a high-quality approximate posterior. I apply these tools to obtain a priori guarantees on the approximate posteriors produced by PASS-GLM. Finally, for cases when users must rely on algorithms that do not have a priori accuracy guarantees, I develop a method for comparing the quality of the inferences produced by competing algorithms. The method comes equipped with provable guarantees while also being computationally efficient.
Book Synopsis Accelerating Monte Carlo methods for Bayesian inference in dynamical models by : Johan Dahlin
Download or read book Accelerating Monte Carlo methods for Bayesian inference in dynamical models written by Johan Dahlin and published by Linköping University Electronic Press. This book was released on 2016-03-22 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: Making decisions and predictions from noisy observations are two important and challenging problems in many areas of society. Some examples of applications are recommendation systems for online shopping and streaming services, connecting genes with certain diseases and modelling climate change. In this thesis, we make use of Bayesian statistics to construct probabilistic models given prior information and historical data, which can be used for decision support and predictions. The main obstacle with this approach is that it often results in mathematical problems lacking analytical solutions. To cope with this, we make use of statistical simulation algorithms known as Monte Carlo methods to approximate the intractable solution. These methods enjoy well-understood statistical properties but are often computational prohibitive to employ. The main contribution of this thesis is the exploration of different strategies for accelerating inference methods based on sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC). That is, strategies for reducing the computational effort while keeping or improving the accuracy. A major part of the thesis is devoted to proposing such strategies for the MCMC method known as the particle Metropolis-Hastings (PMH) algorithm. We investigate two strategies: (i) introducing estimates of the gradient and Hessian of the target to better tailor the algorithm to the problem and (ii) introducing a positive correlation between the point-wise estimates of the target. Furthermore, we propose an algorithm based on the combination of SMC and Gaussian process optimisation, which can provide reasonable estimates of the posterior but with a significant decrease in computational effort compared with PMH. Moreover, we explore the use of sparseness priors for approximate inference in over-parametrised mixed effects models and autoregressive processes. This can potentially be a practical strategy for inference in the big data era. Finally, we propose a general method for increasing the accuracy of the parameter estimates in non-linear state space models by applying a designed input signal. Borde Riksbanken höja eller sänka reporäntan vid sitt nästa möte för att nå inflationsmålet? Vilka gener är förknippade med en viss sjukdom? Hur kan Netflix och Spotify veta vilka filmer och vilken musik som jag vill lyssna på härnäst? Dessa tre problem är exempel på frågor där statistiska modeller kan vara användbara för att ge hjälp och underlag för beslut. Statistiska modeller kombinerar teoretisk kunskap om exempelvis det svenska ekonomiska systemet med historisk data för att ge prognoser av framtida skeenden. Dessa prognoser kan sedan användas för att utvärdera exempelvis vad som skulle hända med inflationen i Sverige om arbetslösheten sjunker eller hur värdet på mitt pensionssparande förändras när Stockholmsbörsen rasar. Tillämpningar som dessa och många andra gör statistiska modeller viktiga för många delar av samhället. Ett sätt att ta fram statistiska modeller bygger på att kontinuerligt uppdatera en modell allteftersom mer information samlas in. Detta angreppssätt kallas för Bayesiansk statistik och är särskilt användbart när man sedan tidigare har bra insikter i modellen eller tillgång till endast lite historisk data för att bygga modellen. En nackdel med Bayesiansk statistik är att de beräkningar som krävs för att uppdatera modellen med den nya informationen ofta är mycket komplicerade. I sådana situationer kan man istället simulera utfallet från miljontals varianter av modellen och sedan jämföra dessa mot de historiska observationerna som finns till hands. Man kan sedan medelvärdesbilda över de varianter som gav bäst resultat för att på så sätt ta fram en slutlig modell. Det kan därför ibland ta dagar eller veckor för att ta fram en modell. Problemet blir särskilt stort när man använder mer avancerade modeller som skulle kunna ge bättre prognoser men som tar för lång tid för att bygga. I denna avhandling använder vi ett antal olika strategier för att underlätta eller förbättra dessa simuleringar. Vi föreslår exempelvis att ta hänsyn till fler insikter om systemet och därmed minska antalet varianter av modellen som behöver undersökas. Vi kan således redan utesluta vissa modeller eftersom vi har en bra uppfattning om ungefär hur en bra modell ska se ut. Vi kan också förändra simuleringen så att den enklare rör sig mellan olika typer av modeller. På detta sätt utforskas rymden av alla möjliga modeller på ett mer effektivt sätt. Vi föreslår ett antal olika kombinationer och förändringar av befintliga metoder för att snabba upp anpassningen av modellen till observationerna. Vi visar att beräkningstiden i vissa fall kan minska ifrån några dagar till någon timme. Förhoppningsvis kommer detta i framtiden leda till att man i praktiken kan använda mer avancerade modeller som i sin tur resulterar i bättre prognoser och beslut.
Book Synopsis Scalable Pattern Recognition Algorithms by : Pradipta Maji
Download or read book Scalable Pattern Recognition Algorithms written by Pradipta Maji and published by Springer Science & Business Media. This book was released on 2014-03-19 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the need for a unified framework describing how soft computing and machine learning techniques can be judiciously formulated and used in building efficient pattern recognition models. The text reviews both established and cutting-edge research, providing a careful balance of theory, algorithms, and applications, with a particular emphasis given to applications in computational biology and bioinformatics. Features: integrates different soft computing and machine learning methodologies with pattern recognition tasks; discusses in detail the integration of different techniques for handling uncertainties in decision-making and efficiently mining large biological datasets; presents a particular emphasis on real-life applications, such as microarray expression datasets and magnetic resonance images; includes numerous examples and experimental results to support the theoretical concepts described; concludes each chapter with directions for future research and a comprehensive bibliography.
Book Synopsis Brain-inspired Cognition and Understanding for Next-generation AI: Computational Models, Architectures and Learning Algorithms by : Chenwei Deng
Download or read book Brain-inspired Cognition and Understanding for Next-generation AI: Computational Models, Architectures and Learning Algorithms written by Chenwei Deng and published by Frontiers Media SA. This book was released on 2023-04-19 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Scalable and Causal Bayesian Inference by : Omar Demian Chavez
Download or read book Scalable and Causal Bayesian Inference written by Omar Demian Chavez and published by . This book was released on 2021 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis will focus on two facets of Bayesian estimation. First, we propose methods that can improve parameter estimation in particle filtering when making use of a distributed computing environment by allowing for periodic communication between compute nodes. The periodic communication can improve the embarrassingly parallel version of our particle filter without dramatically increasing the computational costs. Our method is intended for use on data with large N or in streaming settings where latent parameters are changing over time. Secondly, we propose a method for estimating heterogeneous treatment effects in observational studies using transformed response variables via a modification to Bayesian additive regression trees that incorporates a mixture model in the regression error terms
Book Synopsis Machine Learning for Engineers by : Osvaldo Simeone
Download or read book Machine Learning for Engineers written by Osvaldo Simeone and published by Cambridge University Press. This book was released on 2022-09-30 with total page 601 pages. Available in PDF, EPUB and Kindle. Book excerpt: This self-contained introduction contains all students need to start applying machine learning principles to real-world engineering problems.
Book Synopsis Scalable Uncertainty Management by : Salem Benferhat
Download or read book Scalable Uncertainty Management written by Salem Benferhat and published by Springer. This book was released on 2011-10-07 with total page 574 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Conference on Scalable Uncertainty Management, SUM 2011, held in Dayton, OH, USA, in October 2011. The 32 revised full papers and 3 revised short papers presented together with the abstracts of 2 invited talks and 6 “discussant” contributions were carefully reviewed and selected from 58 submissions. The papers are organized in topical sections on argumentation systems, probabilistic inference, dynamic of beliefs, information retrieval and databases, ontologies, possibility theory and classification, logic programming, and applications.
Book Synopsis Bayesian Deep Learning by : Matt Benatan
Download or read book Bayesian Deep Learning written by Matt Benatan and published by . This book was released on 2023-06-30 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis An Introduction to Bayesian Inference, Methods and Computation by : Nick Heard
Download or read book An Introduction to Bayesian Inference, Methods and Computation written by Nick Heard and published by Springer Nature. This book was released on 2021-10-17 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: These lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy and principles of Bayesian inference, including the reasoning behind the prior/likelihood model construction synonymous with Bayesian methods, through to advanced topics such as nonparametrics, Gaussian processes and latent factor models. These advanced modelling techniques can easily be applied using computer code samples written in Python and Stan which are integrated into the main text. Importantly, the reader will learn methods for assessing model fit, and to choose between rival modelling approaches.
Book Synopsis Pattern Recognition and Machine Intelligence by : Santanu Chaudhury
Download or read book Pattern Recognition and Machine Intelligence written by Santanu Chaudhury and published by Springer. This book was released on 2009-12-15 with total page 650 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the proceedings of the third international conference on Pattern Recognition and Machine Intelligence (PReMI 2009) which was held at the Indian Institute of Technology, New Delhi, India, during December 16–20, 2009. This was the third conference in the series. The first two conferences were held in December at the Indian Statistical Institute, Kolkata in 2005 and 2007. PReMI has become a premier conference in India presenting state-of-art research findings in the areas of machine intelligence and pattern recognition. The conference is also successful in encouraging academic and industrial interaction, and in prom- ing collaborative research and developmental activities in pattern recognition, - chine intelligence and other allied fields, involving scientists, engineers, professionals, researchers and students from India and abroad. The conference is scheduled to be held every alternate year making it an ideal platform for sharing views and expe- ences in these fields in a regular manner. The focus of PReMI 2009 was soft-computing, machine learning, pattern recognition and their applications to diverse fields. As part of PReMI 2009 we had two special workshops. One workshop focused on text mining. The other workshop show-cased industrial and developmental projects in the relevant areas. Premi 2009 attracted 221 submissions from different countries across the world.
Book Synopsis Bayesian Inference on Complicated Data by : Niansheng Tang
Download or read book Bayesian Inference on Complicated Data written by Niansheng Tang and published by BoD – Books on Demand. This book was released on 2020-07-15 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to great applications in various fields, such as social science, biomedicine, genomics, and signal processing, and the improvement of computing ability, Bayesian inference has made substantial developments for analyzing complicated data. This book introduces key ideas of Bayesian sampling methods, Bayesian estimation, and selection of the prior. It is structured around topics on the impact of the choice of the prior on Bayesian statistics, some advances on Bayesian sampling methods, and Bayesian inference for complicated data including breast cancer data, cloud-based healthcare data, gene network data, and longitudinal data. This volume is designed for statisticians, engineers, doctors, and machine learning researchers.
Book Synopsis Artificial Intelligence in Marketing by : K. Sudhir
Download or read book Artificial Intelligence in Marketing written by K. Sudhir and published by Emerald Group Publishing. This book was released on 2023-03-13 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Review of Marketing Research pushes the boundaries of marketing—broadening the marketing concept to make the world a better place. Here, leading scholars explore how marketing is currently shaping, and being shaped by, the evolution of Artificial Intelligence (AI).
