Machine Learning¶
Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.
Cited by¶
4 citations across 4 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Bayesian Updating
- In machine learning, Bayesian methods underpin Gaussian processes, Bayesian neural networks, variational autoencoders, and much of probabilistic programming (Stan, PyMC, Turing.jl)
This sourceDevelops Bayesian machine learning in depth — Gaussian processes (ch. 15), Bayesian neural networks / adaptive basis models (ch. 16), and variational inference (chs. 21–22) — substantiating the claim that Bayesian methods underpin modern probabilistic ML. SUPPORTS marker 009 (re-sourced from pearl-1988, which predates these methods).
- In machine learning, Bayesian methods underpin Gaussian processes, Bayesian neural networks, variational autoencoders, and much of probabilistic programming (Stan, PyMC, Turing.jl)
- Belief Formation
- When the system mis-forms (a false positive blocks a legitimate transaction; a false negative allows a fraudulent one), the diagnostic vocabulary applies: was the prior miscalibrated, was an input missing or noisy, was the mechanism over-confident, did the posterior threshold trigger inappropriate downstream action — a kind of probabilistic-belief failure mode Murphy (2012) catalogues across canonical ML model families.
This sourceCanonical ML reference unifying generative models, Bayesian inference, and PGMs, and cataloguing failure modes of probabilistic-belief representations (miscalibration, prior misspecification)
- When the system mis-forms (a false positive blocks a legitimate transaction; a false negative allows a fraudulent one), the diagnostic vocabulary applies: was the prior miscalibrated, was an input missing or noisy, was the mechanism over-confident, did the posterior threshold trigger inappropriate downstream action — a kind of probabilistic-belief failure mode Murphy (2012) catalogues across canonical ML model families.
- Overfitting
- Industry practice — walk-forward validation, parameter stability tests, stress testing against alternative periods, preference for simpler rules with fewer parameters, out-of-sample trading periods before deployment — are direct responses to this failure mode
This sourceCanonical ML reference unifying generative models, Bayesian inference, and probabilistic graphical models; catalogues failure modes of probabilistic-belief representations (miscalibration, prior misspecification, mode collapse) across model families.
- Industry practice — walk-forward validation, parameter stability tests, stress testing against alternative periods, preference for simpler rules with fewer parameters, out-of-sample trading periods before deployment — are direct responses to this failure mode
- Regularization
- MAP estimation is penalized maximum likelihood; lasso is MAP under a Laplace prior; ridge is MAP under a Gaussian prior.
This sourceEstablishes the prior–penalty duality: MAP estimation as penalized maximum likelihood, lasso as a Laplace prior, ridge as a Gaussian prior.
- MAP estimation is penalized maximum likelihood; lasso is MAP under a Laplace prior; ridge is MAP under a Gaussian prior.
Verification¶
This reference passed the adversarial substantiation pipeline: it was checked to exist and to support the claim it is attached to. See how references were verified.
Links previously used in the corpus¶
Before the registry existed this work was also linked 1 other way.
Registry ID ref:0add1bf8f2c8 · see in the full table