SKU/Artículo: AMZ-B0FL1CN6BM
Bayesian Analysis: Theorems, Proofs, and Python Implementations (Computational Mathematics Library)
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Paperback
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.99 kg
0.99 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
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USA
USA
Sobre este producto
- The complete graduate-level reference for Bayesian statistics, MCMC, and variational inference—each chapter paired with executable Python code. • All the mathematics you need. Twenty-four tightly written chapters walk from σ-algebras and Radon–Nikodym derivatives to state-of-the-art Hamiltonian Monte Carlo, Gaussian processes, and Bayesian deep learning. • Code you can run today. Every chapter concludes with reproducible Python scripts that implement the theorems and examples in NumPy, SciPy, PyMC, and JAX. • High-impact topics. Coverage aligns with the most searched phrases in Bayesian data science:Markov Chain Monte Carlo (Metropolis-Hastings, Gibbs, Hamiltonian, Slice, Reversible Jump)Variational Bayes and stochastic gradient algorithmsDirichlet and Pitman–Yor processes for nonparametric clusteringBayesian neural networks and probabilistic programmingPosterior contraction, Bernstein–von Mises, and high-dimensional sparsityMarginal likelihoods, Bayes factors, and model selectionSequential Monte Carlo and particle MCMC for time-series models
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AR$171.203
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AR$333.838
49% OFF
AR$171.203
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