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Probabilistic Forecasting and Bayesian Data Assimilation

In this book the authors describe the principles and methods behind probabilistic forecasting and Bayesian data assimilation. Instead;

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Probabilistic Forecasting and Bayesian Data Assimilation

In this book the authors describe the principles and methods behind probabilistic forecasting and Bayesian data assimilation. Instead;

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Bayesian Analysis with Python

data analyses using PyMC3 and ArviZ A modern, practical and computational approach to Bayesian statistical modeling A tutorial for Bayesian;

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Probabilistic Methods for Bioinformatics

> Rather than getting bogged down in proofs and algorithms, probabilistic methods used for biological information and Bayesian networks are;

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Bayesian Networks Handbook

probabilistic semantics; and lastly, this method along with statistical method provides efficient and balanced approach to avoid over fitting of data;

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Ecological Forecasting

, including Bayesian calibration and the complexities of real-world data; uncertainty quantification, partitioning, propagation, and analysis;

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Developments in Demographic Forecasting

This open access book presents new developments in the field of demographic forecasting, covering both mortality, fertility and migration;

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Bayesian Inference for Probabilistic Risk Assessment

Bayesian Inference for Probabilistic Risk Assessment provides a Bayesian foundation for framing probabilistic problems and performing;

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Cambridge Series in Statistical and Probabilistic Mathematics

This book describes the Bayesian approach to statistics at a level suitable for final year undergraduate and Masters students. It;

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Cambridge Series in Statistical and Probabilistic Mathematics

This book describes the Bayesian approach to statistics at a level suitable for final year undergraduate and Masters students. It;

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Bayesian Programming

. Written by the team who designed and implemented an efficient probabilistic inference engine to interpret Bayesian programs, the book offers many;

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Bayesian Programming

. Written by the team who designed and implemented an efficient probabilistic inference engine to interpret Bayesian programs, the book offers many;

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Stochastic Causality

The papers collected here focus on probabilistic causality, addressing topics such as the search for causal mechanisms, epistemic and;

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Bayesian Analysis with Python

Students, researchers and data scientists who wish to learn Bayesian data analysis with Python and implement probabilistic models in their day to;

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Bayesian Demographic Estimation and Forecasting

Bayesian Demographic Estimation and Forecasting presents three statistical frameworks for modern demographic estimation and forecasting;

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Bayesian Demographic Estimation and Forecasting

Bayesian Demographic Estimation and Forecasting presents three statistical frameworks for modern demographic estimation and forecasting;

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Data-Driven Computational Neuroscience

of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial;

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Bayesian Forecasting and Dynamic Models

This text is concerned with Bayesian learning, inference and forecasting in dynamic environments. We describe the structure and theory;

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Applied Bayesian Forecasting and Time Series Analysis

Practical in its approach, Applied Bayesian Forecasting and Time Series Analysis provides the theories, methods, and tools necessary for;

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Applied Bayesian Forecasting and Time Series Analysis

Practical in its approach, Applied Bayesian Forecasting and Time Series Analysis provides the theories, methods, and tools necessary for;

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Bayesian Inverse Problems

Gain a fundamental understanding of the use of probabilistic methods and reliability analysis techniques in engineering applications;

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Bayesian Compendium

exactly should data be used in modelling? The literature offers a bewildering variety of techniques and approaches (Bayesian calibration, data;

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Bayesian Compendium

exactly should data be used in modelling? The literature offers a bewildering variety of techniques and approaches (Bayesian calibration, data;

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Economic Forecasting

Economic forecasting involves choosing simple yet robust models to best approximate highly complex and evolving data-generating processes;

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Bayesian and grAphical Models for Biomedical Imaging

This book constitutes the refereed proceedings of the First International Workshop on Bayesian and grAphical Models for Biomedical Imaging;

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Building the European Capacity in Operational Oceanography

Oceanography Series. The topics of the proceedings cover: Regional Systems I, Remote Sensing Systems, Numerical Modelling and Data Assimilation, Next;

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Computational Learning And Probabilistic Reasoning

provide computational probabilistic models for manipulating knowledge found in industrial and business data. These methods are very efficient for;

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