Chapter 00: IntroductionUpdated 4 days ago

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Chapter 05: Model predictions and posterior predictive checks (+ bayesplot ppc_*)Updated 4 days ago

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Chapter 06: Deviance residuals, model statistics and posterior inference (+ bayestestR)Updated 4 days ago

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Chapter 12: Visualizing posteriors with bayesplotUpdated 4 days ago

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Chapter 13: Bayesian inference and decision making with bayestestRUpdated 4 days ago

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Chapter 02-S02: Normal–Normal Conjugacy for One MeanUpdated 4 days ago

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Chapter 02-S03: Beta–Binomial Conjugacy for One ProportionUpdated 4 days ago

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Chapter 02-S04: Gamma–Poisson Conjugacy for One Count RateUpdated 4 days ago

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Chapter 02-S05: Gamma–Gamma Conjugacy for One Response RateUpdated 4 days ago

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Chapter 10: Models for the Poisson familyUpdated 4 days ago

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Chapter 01: Setting Up OpenCL and Enabling GPU AccelerationUpdated 4 days ago

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Chapter 02: Adding USE_OPENCL and has_opencl() to Your PackageUpdated 4 days ago

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Chapter 03: Structure of nmath Kernel ProgramsUpdated 4 days ago

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Chapter 05: Kernels, Kernel Runners, and Kernel WrappersUpdated 4 days ago

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Chapter 06: Integrating Kernel Wrappers into Your CodebaseUpdated 4 days ago

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Chapter 08: Kernel Loading --- load_kernel_source and load_kernel_libraryUpdated 4 days ago

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Chapter 10: Case Study --- Building Custom GLM Kernels (ex_glmbayes)Updated 4 days ago

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Chapter 01: Getting started — Setting up OpenCLUpdated 5 days ago

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Chapter 02: Using a ported library — assembling kernel programsUpdated 5 days ago

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Chapter 03: Kernel runners and wrappers — the glmbayes patternUpdated 5 days ago

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Chapter 01: Getting started with glmbayesUpdated 7 days ago

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Chapter 02-S01: Conjugate Models — Introduction and OverviewUpdated 7 days ago

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Chapter 03: Estimating Bayesian linear modelsUpdated 7 days ago

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Chapter 08: Estimating Bayesian generalized linear modelsUpdated 7 days ago

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Chapter 09: Models for the Binomial familyUpdated 7 days ago

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Chapter 11: Models for the Gamma familyUpdated 7 days ago

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Chapter 04: Tailoring priors — leveraging the Prior_Setup functionUpdated 8 days ago

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Chapter 07: Foundations of GLMs — families, links, and log-concave likelihoodsUpdated 8 days ago

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Chapter 15: Estimating models with unknown dispersion parametersUpdated 8 days ago

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Chapter A01: A detailed overview of the glmbayes packageUpdated 8 days ago

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Chapter A12: Technical Derivations for Priors Returned by `Prior_Setup()Updated 8 days ago

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Chapter 14: Informative priors — centering and differential prior weightsUpdated 9 days ago

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Chapter 16: Large models — GPU acceleration using OpenCLUpdated 9 days ago

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Chapter 17: Linear mixed-effects modelsUpdated 9 days ago

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Chapter 18: Generalized linear mixed-effects modelsUpdated 9 days ago

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Chapter A10: Accelerated EnvelopeBuild Implementation using OpenCLUpdated 9 days ago

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Chapter 00: nmathopencl --- Package OverviewUpdated 9 days ago

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Chapter 04: The nmath OpenCL LibraryUpdated 9 days ago

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Chapter 07: Kernels --- Writing and Using OpenCL Kernel FilesUpdated 9 days ago

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Chapter 11: Testing, Debugging, and Benchmarking GPU KernelsUpdated 9 days ago

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Chapter 12: The nmathopencl R API --- Distribution Functions on the GPUUpdated 9 days ago

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Chapter 09: Generic OpenCL Kernel Runners (openclPort layer)Updated 9 days ago

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Chapter A11: Implementation Companion for Independent Normal-GammaUpdated 2 months ago

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Chapter A02: Overview of Estimation ProceduresUpdated 2 months ago

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Chapter A04: Directional Tail Diagnostics for Prior-Posterior DisagreementUpdated 2 months ago

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Chapter A06: Accept–Reject Sampling for Dispersion in Gamma RegressionUpdated 2 months ago

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Chapter A03: Methods available in glmbayesUpdated 2 months ago

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Chapter A05: Simulation Methods - Likelihood Subgradient DensitiesUpdated 2 months ago

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Chapter A08: Overview of Envelope Related FunctionsUpdated 2 months ago

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Chapter A07: Accept–Reject Sampling for gaussian Regression models with independent normal-gamma priorsUpdated 2 months ago

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Chapter A09: Parallel Sampling Implementation using RcppParallelUpdated 2 months ago

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