Results for “pyopenms”

Process and analyze mass spectrometry data with the Matchms Python library, including importing spectra, filtering peaks, calculating similarity scores, and building reproducible analytical workflows.

Generates Python code using BigQuery DataFrames (BigFrames), the pandas/scikit-learn-style API over BigQuery, for dataframe and ML workflows.

Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.

Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.

Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.

Use for writing, reviewing, debugging, testing, or diagnosing Python Bayesian models built directly with PyMC, including Model, coords/dims, Data, random variables, potentials, posterior sampling, prior/posterior predictive checks, and InferenceData output. Trigger on model geometry, shape errors, divergences, sampler choice, mutable prediction data, and probabilistic validation. Do not use for Bambi formula models, NumPyro/JAX programs, ArviZ-only analysis of existing draws, deterministic optimization, or general statistics without PyMC code.

Build, fit, validate, and compare Bayesian models using PyMC, including hierarchical models, MCMC sampling, variational inference, posterior predictive checks, and model comparison.

Solve single and multi-objective optimization problems using NSGA-II/III, MOEA/D, and other evolutionary algorithms with customizable operators, constraint handling, and benchmark problems.

Build, fit, validate, and compare Bayesian models using PyMC's modern API, including hierarchical models, MCMC sampling, variational inference, posterior predictive checks, and model comparison.

Solves single- and multi-objective optimization problems with NSGA-II/III, MOEA/D, and other evolutionary algorithms, including constraint handling, Pareto front analysis, and benchmark problems.

Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.

Bayesian modeling and probabilistic programming with PyMC — hierarchical models, MCMC (NUTS) sampling, variational inference, LOO/WAIC model comparison, and posterior predictive checks. Use when fitting Bayesian or hierarchical models, estimating posteriors and credible intervals, running probabilistic inference, or comparing models with LOO/WAIC. Part of the AlterLab Academic Skills suite.

Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.

Provides guidance on using PyTorch for deep learning, covering tensors, autograd, nn.Module, DataLoaders, and best practices.