'Bayesian models' diaporamas de présentation

Bayesian models of inductive learning

4.13k views • 384 slides

Bayesian models of inductive learning

3.84k views • 384 slides

Bayesian models of inductive learning

3.84k views • 384 slides

Shiffrin Says

1.78k views • 157 slides

Bayesian models of human learning and inference Josh Tenenbaum MIT

Bayesian models of human learning and inference Josh Tenenbaum MIT Department of Brain and Cognitive Sciences Computer Science and AI Lab (CSAIL). (http://web.mit.edu/cocosci/Talks/nips06-tutorial.ppt). Thanks to Tom Griffiths, Charles Kemp, Vikash Mansinghka.

2.04k views • 156 slides

Bayesian models of inductive learning

Bayesian models of inductive learning. Tom Griffiths UC Berkeley. Charles Kemp CMU. Josh Tenenbaum MIT. What you will get out of this tutorial. Our view of what Bayesian models have to offer cognitive science

933 views • 93 slides

Cours 4

984 views • 83 slides

Bayesian models of inductive learning

Bayesian models of inductive learning. Tom Griffiths UC Berkeley. Charles Kemp CMU. Josh Tenenbaum MIT. Outline. Morning 9:00-10:30: Introduction: Why Bayes?; Basics of Bayesian inference (Josh) 11:00-12:30: How to build a Bayesian cognitive model (Tom) Afternoon

813 views • 80 slides

1.12k views • 69 slides

Bayesian Statistics Without Tears: Prelude

Bayesian Statistics Without Tears: Prelude. Eric-Jan Wagenmakers. Three Schools of Statistical Inference. Neyman-Pearson : ? -level, power calculations, two hypotheses, guide for action (i.e., what to do). Fisher : p -values, one hypothesis (i.e., H 0 ), quantifies evidence against H 0 .

799 views • 59 slides

Announcements

Announcements. CS Ice Cream Social 9 /5 3:30-4: 30, ECCR 265 includes poster session, student group presentations. Concept Learning. Examples Word meanings Edible foods Abstract structures (e.g., irony). glorch. not glorch. glorch. not glorch.

399 views • 35 slides

Three challenges for computational models of cognition

Three challenges for computational models of cognition. Charles Kemp CMU. Humans vs machines. Outstanding. Performance. Not so good. Human. Machine. First order of business is to close this gap:

308 views • 18 slides

Preliminary Statistical Comments on Pfizer Gabapentin and Pregabalin Analysis

Preliminary Statistical Comments on Pfizer Gabapentin and Pregabalin Analysis. Mark Levenson, Ph.D. Statistical Safety Reviewer Quantitative Safety and Pharmacoepidemiology Group Division of Biometrics 6/CDER/FDA

233 views • 11 slides

Machine learning methods for the analysis of heterogeneous, multi-source data

Machine learning methods for the analysis of heterogeneous, multi-source data. Ilkka Huopaniemi ilkka.huopaniemi@tkk.fi Statistical machine learning and bioinformatics group Prof. Samuel Kaski Department of information and computer science. Bioinformatics/Metabolomics.

196 views • 5 slides

Responses to Climate across Trophic Levels Joe Sexton University of Maryland

Responses to Climate across Trophic Levels Joe Sexton University of Maryland David Mattson U.S. Geological Survey. Questions What are the spatial, demographic, and energetic responses of terrestrial vertebrates to climate? How do climate effects transmit through the trophic chain?

157 views • 1 slides