Posterior predictive distribution from a Bayesian Pillar 2 fit — piml_bayes_posterior • edaphos

3.10.0

Reference
- Capstone — six pillars integrated
- Cross-pillar campaign
- Pilar 1 — Causal AI
- Causal backdoor on br_cerrado
- Causal on 1 095 real WoSIS profiles
- Causal-discovery trio (expert × LLM × data-driven)
- LLM-KG benchmark (Gemma 4 × GPT × Claude)
- Annotation workflow (pre-annotation + Shiny review)
- Pilar 2 — Physics-Informed ML
- Parametric ODE + Neural ODE
- Pilar 3 — 4D Pedometry
- ConvLSTM on synthetic cube
- Real MODIS + NASA POWER + EnKF
- Pilar 4 — Foundation Models
- SimCLR / MoCo embeddings
- Pilar 5 — Active Learning
- Hybrid policy + physics gate
- SoilGrids-BR live
- Pilar 6 — Quantum ML
- ZZFeatureMap + Q-KRR
- Cross-pillar bridges
- Pilar 1 × Pilar 4 — Foundation IVs
- Pilar 4 × Pilar 6 — Quantum-foundation kernel
- Unified infrastructure
- Unified uncertainty API
- Case — end-to-end Cerrado benchmark
- Releases
- v2.0.0 — Quantum kernel × Foundation (Pilar 4 × Pilar 6)
- v1.9.x — Foundation embeddings as causal IVs + Sargan/Cinelli-Hazlett
- v1.8.x — LLM benchmark infrastructure + Shiny annotation tool
- v1.7.x — Capstone vignette + native-query calibration + causal discovery
- Changelog

Convenience wrapper that calls predict.edaphos_piml_bayes() on the requested depths with either the Laplace or the MCMC posterior samples, and returns the result as an edaphos_posterior().

Usage

piml_bayes_posterior(
  object,
  newdepths,
  n_draws = NULL,
  include_obs_noise = FALSE,
  seed = NULL,
  units = NULL
)

Arguments

object: An edaphos_piml_bayes (Laplace or MCMC).
newdepths: Numeric vector of depths.
n_draws: Integer — number of posterior draws to keep from the underlying chain. Defaults to min(500, nrow(object$draws)).
include_obs_noise: Logical — see piml_neural_ode_posterior().
seed: Optional RNG seed.
units: Optional units tag.

Value

An edaphos_posterior with method = "bayesian".