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Bayesian Modeling and Computation in Python

Bayesian Modeling and Computation in Python aims to help beginner Bayesian practitioners to become intermediate modelers. It uses a hands;

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Essential Bayesian Models

This accessible reference includes selected contributions from Bayesian Thinking - Modeling and Computation, Volume 25 in the Handbook;

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Bayesian Analysis with Python

Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state;

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Statistical Modeling and Computation

Modeling and Computation provides a unique introduction to modern Statistics from both classical and Bayesian perspectives. It also offers an;

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Bayesian Thinking, Modeling and Computation

This volume describes how to develop Bayesian thinking, modelling and computation both from philosophical, methodological and application;

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An Introduction to Bayesian Inference, Methods and Computation

and principles of Bayesian inference, including the reasoning behind the prior/likelihood model construction synonymous with Bayesian methods;

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Statistical Computation for Environmental Sciences in R

. In Models for Ecological Data, James Clark introduces ecologists to these modern methods in modeling and computation. Assuming only basic courses;

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Bayesian Disease Mapping

, clustering, spatial survival analysis, and longitudinal analysis. After an introduction to Bayesian inference, computation, and model assessment, the;

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Bayesian Disease Mapping

, clustering, spatial survival analysis, and longitudinal analysis. After an introduction to Bayesian inference, computation, and model assessment, the;

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Bayesian Model Comparison

issues include parallel computation, Hamiltonian Monte Carlo, dynamic model selection, small sample comparison of structural models, Bayesian;

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Using R for Bayesian Spatial and Spatio-Temporal Health Modeling

Overview of Bayesian methods and Bayesian hierarchical modeling as applied to spatial data Bayesian Computation and goodness-of-fit;

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Time Series

Focusing on Bayesian approaches and computations using analytic and simulation-based methods for inference, Time Series: Modeling;

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Bayesian Methods For Hackers

Master Bayesian Inference through Practical Examples and Computation-Without Advanced Mathematical Analysis Bayesian methods;

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Models for Ecological Data

. In Models for Ecological Data, James Clark introduces ecologists to these modern methods in modeling and computation. Assuming only basic courses;

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Time Series

Focusing on Bayesian approaches and computations using simulation-based methods for inference, Time Series: Modeling, Computation, and;

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Bayesian Programming

financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an;

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Bayesian Programming

financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an;

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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 Data Analysis

students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it;

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Bayesian Models for Astrophysical Data

This comprehensive guide to Bayesian methods in astronomy enables hands-on work by supplying complete R, JAGS, Python, and Stan code, to;

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Bayesian Modeling Using WinBUGS

implementation of key principles. The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key;

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Bayesian Modeling in Bioinformatics

Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high;

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Bayesian Modeling in Bioinformatics

Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high;

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Statistics for Astrophysics

Computation (ABC) and Bayesian nonparametric inference and clustering.;

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Think Complexity 2e

Complexity science uses computation to explore the physical and social sciences. In Think Complexity, you'll use graphs, cellular automata;

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Bayesian Disease Mapping

Focusing on data commonly found in public health databases and clinical settings, Bayesian Disease Mapping: Hierarchical Modeling;

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Advances in Artificial Intelligence

papers are organized in topical sections on Bayesian networks and uncertainty modeling; fuzzy logic and soft computing; knowledge representation;

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