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Bayesian Inference is known to make machines bi...

αλεx π
November 24, 2015

Bayesian Inference is known to make machines biased

Humans have many biases. Let's call them priors. They also have their expertises. Let's call them hypothesis spaces. We often make the conclusions we make swiftly, and without much effort and call it intuition. How do we make machines reason intuitively and what it may end with.

Examples will be given in Probabilistic Programming framework called Anglican, successor of the Goodman & Tenenbaum Church language.

αλεx π

November 24, 2015
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  1. 1. Define domain of inputs 2. Generate inputs over domain

    and it’s PD 3. Perform a deterministic computation 4. Aggregate
  2. Machine Learning: a Probabilistic Perspective https://www.cs.ubc.ca/~murphyk/MLbook/ Bayesian models of cognition

    https://cocosci.berkeley.edu/tom/papers/bayeschapter.pdf Bayesian Methods for Hackers https://github.com/CamDavidsonPilon/Probabilistic-Programming-and- Bayesian-Methods-for-Hackers Anglican http://www.robots.ox.ac.uk/~fwood/anglican/index.html Introduction to Markov Chain Monte Carlo http://www.mcmchandbook.net/HandbookChapter1.pdf Reading List