Doing Bayesian Data Analysis

By John K. Kruschke. 2010

423 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/57a275487457feee4fe07fa0cd6f6a2ac9e4d1a0

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In Artificial Intelligence

Citations in the corpus

A Non-Stationary 1981-2012 AVHRR NDVI3g Time Series (2014, methodology): “Monte Carlo methods provide us with a powerful tool for simulating NDVI distributions and creating a model of these data [67,75].”
When (ish) is My Bus?: User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive Systems (2016, methodology): “We use a Bayesian model, which allows us to build on previous results by specifying prior information for effects, and report results primarily as posterior distributions with 95% credibility intervals (the Bayesian analog to a confidence interval) [24,25].”
A parametric texture model based on deep convolutional features closely matches texture appearance for humans (2017, methodology): “While the priors we use here are informed by the scale of the model and by common practice for Bayesian regression models (see for example Gelman, 2006; Gelman & Hill, 2007; Kruschke, 2011), the speciﬁc choices we make are somewhat arbitrary.”
A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research (2013, neutral): “Advantages of Bayesian statistics over frequentist statistics are well documented in the literature (Jaynes, 2003; Kaplan & Depaoli, 2012, 2013; Kruschke, 2011a, 2011b; Lee & Wagen-makers, 2005; Van de Schoot, Verhoeven, & Hoijt-ink, 2012; Wagenmakers, 2007) and we will just highlight some of those…”
Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data. (2016, background): “4 Detailed treatments of Bayesian statistics be found in many available textbooks, for example in O'Hagan (1994), Gelman et al. (2013), Jaynes (2003), and Kruschke (2014) .”