Bayesian Inference

In an era where data is abundant and computational power is soaring, Bayesian Inference - Recent Trends emerges as an essential guide to understanding and applying Bayesian methods in various scientific and technological domains. This book uniquely blends theoretical rigor with practical insights, s...

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Հրապարակվել է: IntechOpen 2024
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Առցանց հասանելիություն:ONIX_20240307_9781837693559_54
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collection Directory of Open Access Books
description In an era where data is abundant and computational power is soaring, Bayesian Inference - Recent Trends emerges as an essential guide to understanding and applying Bayesian methods in various scientific and technological domains. This book uniquely blends theoretical rigor with practical insights, showcasing the latest advancements and applications of Bayesian inference. • Discover the renaissance of Bayesian inference and its vital role in modern-day statistical analysis and prediction. • Explore the depth of hidden Markov models and their power in inferring hidden states and transitions in stochastic systems. • Dive into the complexity of nested sampling and its effectiveness in parameter estimation, particularly in signal processing. • Examine the precision of naive Bayes algorithms in news classification, a critical task in the digital information age. This book is an invaluable resource for anyone interested in the intersection of statistics, machine learning, and data science. It offers a unique perspective on Bayesian inference, revealing its potential to provide robust solutions in an increasingly data-driven world. Whether you are a seasoned researcher, a budding scientist, or a curious enthusiast, Bayesian Inference - Recent Trends is your gateway to understanding and leveraging the power of Bayesian methods in the ever-evolving landscape of data analysis.
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spelling doab-20.500.12854ir-1351462024-04-04T14:41:14Z Bayesian Inference Ömür Bucak, İhsan Bayes Theorem Conditional Probability Prior Probability Prior Distribution Markov Chain Monte Carlo Methods Bayes Rule Bayesian Estimators Bayesian Approach vs. Frequentist Approach Bayesian Inference vs. Machine Learning Classical vs. Bayesian Inference Real Life Examples of Bayesian Inference Applications in Stock Market Prediction thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics In an era where data is abundant and computational power is soaring, Bayesian Inference - Recent Trends emerges as an essential guide to understanding and applying Bayesian methods in various scientific and technological domains. This book uniquely blends theoretical rigor with practical insights, showcasing the latest advancements and applications of Bayesian inference. • Discover the renaissance of Bayesian inference and its vital role in modern-day statistical analysis and prediction. • Explore the depth of hidden Markov models and their power in inferring hidden states and transitions in stochastic systems. • Dive into the complexity of nested sampling and its effectiveness in parameter estimation, particularly in signal processing. • Examine the precision of naive Bayes algorithms in news classification, a critical task in the digital information age. This book is an invaluable resource for anyone interested in the intersection of statistics, machine learning, and data science. It offers a unique perspective on Bayesian inference, revealing its potential to provide robust solutions in an increasingly data-driven world. Whether you are a seasoned researcher, a budding scientist, or a curious enthusiast, Bayesian Inference - Recent Trends is your gateway to understanding and leveraging the power of Bayesian methods in the ever-evolving landscape of data analysis. 2024-03-07T16:45:06Z 2024-03-07T16:45:06Z 2024 book ONIX_20240307_9781837693559_54 9781837693559 9781837693573 9781837693566 https://directory.doabooks.org/handle/20.500.12854/135146 eng image/jpeg n/a https://www.intechopen.com/books/1002579 https://intech-files.s3.amazonaws.com/a043Y00000yJC5bQAG/0014483_Authors_Book%20%282024-01-25%2010%3A25%3A46%29.pdf IntechOpen IntechOpen 10.5772/intechopen.1000345 10.5772/intechopen.1000345 78a36484-2c0c-47cb-ad67-2b9f5cd4a8f6 9781837693559 9781837693573 9781837693566 IntechOpen 88 open access
spellingShingle Bayes Theorem
Conditional Probability
Prior Probability
Prior Distribution
Markov Chain Monte Carlo Methods
Bayes Rule
Bayesian Estimators
Bayesian Approach vs. Frequentist Approach
Bayesian Inference vs. Machine Learning
Classical vs. Bayesian Inference
Real Life Examples of Bayesian Inference
Applications in Stock Market Prediction
thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics
Bayesian Inference
title Bayesian Inference
title_full Bayesian Inference
title_fullStr Bayesian Inference
title_full_unstemmed Bayesian Inference
title_short Bayesian Inference
title_sort bayesian inference
topic Bayes Theorem
Conditional Probability
Prior Probability
Prior Distribution
Markov Chain Monte Carlo Methods
Bayes Rule
Bayesian Estimators
Bayesian Approach vs. Frequentist Approach
Bayesian Inference vs. Machine Learning
Classical vs. Bayesian Inference
Real Life Examples of Bayesian Inference
Applications in Stock Market Prediction
thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics
topic_facet Bayes Theorem
Conditional Probability
Prior Probability
Prior Distribution
Markov Chain Monte Carlo Methods
Bayes Rule
Bayesian Estimators
Bayesian Approach vs. Frequentist Approach
Bayesian Inference vs. Machine Learning
Classical vs. Bayesian Inference
Real Life Examples of Bayesian Inference
Applications in Stock Market Prediction
thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics
url ONIX_20240307_9781837693559_54