Handy Lexicon - Data4DM/BayesSD GitHub Wiki

List of pure Bayes lexicon is in https://statmodeling.stat.columbia.edu/2009/05/24/handy_statistic/

Pinoccio

  • sin vs exponential grwoth?
  • multimodality might be not relevant to your problem image

Dynamic aggregation

  • to prevent gardens of forking path in modeling hetereogenity (relevant to Best cluster may not come from clustering)

Glass ceiling

  • wrongly set upper bound of parameter hampering the estimation by cutting off the head

Loop2Table

  • Using table function as loop's abstraction: functional mapping (SW vs base point parameter
  • Tom is also using "parameterize table functions for sensitivity testing" for his deer chronic disease aging chain, but Tom is experiencing parameter's upper bound acting as a ceiling
  • relevant to Loop knockout, causal identification as described in

Rainbow

  • somewhere in between

Scale free

  • Tom showed me the picture of waterfall
  • only nature can produce full fractal

Two ways to improve SIR: coflow and SEIR

  • screen function

Cooking Time series for dynamic modeling

  • 0_PA, 1_PAD, 2_PD, 3_Data4DM, 4_DM4Data
  • labeled then unlabeled

Description: finding patterns

  • descriptive
  • visualization
  • clustering
  • latent variable identification & generative approaches
    • Bayesian (theory-based): HMM, Particle filtering, PMCMC
    • Connectionist (less theory-based): Autoencoders, VAEs, GANs
  • Dimensionality reduction (PCA/ICA, t-sne, SVC)
  • Causality detection (CCM)
  • Density estimation

Prediction: Identify systemic way to anticipate outcome

  • regression
  • classification key: defining loss function and regularization

Causal Prediction: Understanding counterfactuals and general behaviors

  • correlation doesn't imply causation
  • seeks to rigorusly predict outcomes in accordance w/posited causal structure
  • advantage
    • capacity to reason about counterfactuals
    • strong generalizability across contexts
    • enhanced explainability
  • heavy reliance upon postulated causal structure
  • can cross-check causal expectations using empirical data via conditional independence, reverse dependence
  • in temporal settings, causal hints can be suggested by empirical data (CCM)

Nathaniel explains the above as: Description (unsupervised), Prediction (supervised/semi-supervised), Causal prediction (both supervised & unsupervised).