Plugins
1 pluginResults for “poster”
14 skills
k-dense-ai Pymc
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.
30.2k · bundle
inference-sh Qwen Image 2 Pro
Generate images with Alibaba Qwen-Image-2.0-Pro via inference.sh CLI, with professional text rendering and fine-grained realism for posters, banners, and text-heavy designs.
584
alterlab-ieu Alterlab Pymc
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.
60 · bundle
levalencia Pymc
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
3 · bundle
conardli Gpt Image 2
Generates and edits images using GPT Image 2 across three modes: direct generation via OpenAI-compatible API, prompt engineering for host-native image tools, or pure prompt advisory. Includes 80+ structured templates for posters, UI mockups, product visuals, maps, slides, and more.
9.2k · bundle
timlai666 Pymc Bayesian Modeling
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
1 · bundle
More results
metinduraktr-44 Pymc Bayesian Modeling
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
0 · bundle
chen-yu-hao Pymc Bayesian Modeling
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
5 · bundle
schattenspiegel Pymc Python
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.
0 · bundle
samuraigpt Muapi Selfie With Celebrities
Generates a realistic behind-the-scenes selfie of the user with a celebrity from a specified movie, with an option to create a cinematic video connecting multiple selfies.
3.7k
vvieira010-pixel Dual Coding Designer
Design a visual complement to verbal content using dual coding principles for stronger encoding. Use when creating slides, diagrams, posters, or visual explanations of complex concepts.
0
lingxling Pymc
Build, fit, validate, and compare Bayesian models using PyMC, including hierarchical models, MCMC sampling, variational inference, posterior predictive checks, and model comparison.
253 · bundle
gabrielmoreira Fine Mapping
Statistical fine-mapping of GWAS loci using SuSiE, SuSiE-inf, and Approximate Bayes Factors to identify credible sets and posterior inclusion probabilities (PIPs) for causal variant discovery. SuSiE-inf adds an infinitesimal polygenic component for improved calibration at well-powered loci.
17 · bundle
schattenspiegel Arviz Python
Use for writing, reviewing, debugging, or testing Python analysis of Bayesian inference results with ArviZ, including 1.x DataTree groups, legacy InferenceData inputs, xarray dimensions and coordinates, conversion, summaries, R-hat/ESS/MCSE diagnostics, posterior predictive checks, PSIS-LOO, Pareto-k, and model comparison. Trigger on chain/draw shape errors, mislabeled groups, flattened samples, missing log likelihood, or misleading diagnostic claims. Do not use to construct or sample PyMC, NumPyro, or Bambi models, for generic plotting, or for deterministic statistics without Bayesian draws.
0 · bundle