0:08 Introduction
1:19 Probabilistic programming
1:53 Stochastic language ”primitives”
3:06 Bayesian inference
3:27 What is Bayes?
3:57 Inverse probability
4:39 Why Bayes
5:13 The Bayes formula
4:21 Stochastic programs
6:51 Prior distribution
8:12 Likelihood function
8:29 Normal distribution
8:53 Binomial distribution
9:14 Poisson distribution
9:32 Infer values for latent variables
9:54 Posterior distribution
9:47 Probabilistic programming abstracts the inference procedure
10:56 Bayes by hand
12:18 Conjugacy
16:43 Probabilistic programming in Python
17:24 PyMC and its features
19:15 Question: Among the different probabilistic programming libraries, is there a difference in what they have to offer?
20:39 Question: How can one know which likelihood distribution to choose?
21:35 Question: Is there a methodology used to specify the likelihood distribution?
22:30 Example: Building models in PyMC
27:31 Stochastic and deterministic variables
37:11 Observed Random Variables
41:00 Question: To what extent are the features of PyMC supported if compiled in different backends?
41:47 Markov Chain Monte Carlo and Bayesian approximation
43:04 Markov chains
44;19 Reversible Markov chains
45:06 Metropolis sampling
48:00 Hamiltonian Monte Carlo
49:10 Hamiltonian dynamics
50:49 No U-turn Sampler (NUTS)
52:11 Question: How do you know the number of leap frog steps to take?
52:55 Example: Markov Chain Monte Carlo in PyMC
1:13:30 Divergences and how to deal with them
1:15:08 Bayesian Fraction of Missing Information
1:16:25 Potential Scale Reduction
1:17:57 Goodness of fit
1:22:40 Intuitive Bayes course
1:23:09 Question: Do bookmakers use PyMC or Bayesian methods?
1:23:53 Question: How does it work if you have different samplers for different variables?
1:25:09 Question: What route should one take in case of data with many discrete variables and many possible values?
1:25:39 Question: Is there a natural way to use PyMC over a cluster of CPUs?
Timestamps for: Chris Fonnesbeck - Probabilistic Python: An Introduction to Bayesian Modeling with PyMC
0:08 Introduction 1:19 Probabilistic programming 1:53 Stochastic language ”primitives” 3:06 Bayesian inference 3:27 What is Bayes? 3:57 Inverse probability 4:39 Why Bayes 5:13 The Bayes formula 4:21 Stochastic programs 6:51 Prior distribution 8:12 Likelihood function 8:29 Normal distribution 8:53 Binomial distribution 9:14 Poisson distribution 9:32 Infer values for latent variables 9:54 Posterior distribution 9:47 Probabilistic programming abstracts the inference procedure 10:56 Bayes by hand 12:18 Conjugacy 16:43 Probabilistic programming in Python 17:24 PyMC and its features 19:15 Question: Among the different probabilistic programming libraries, is there a difference in what they have to offer? 20:39 Question: How can one know which likelihood distribution to choose? 21:35 Question: Is there a methodology used to specify the likelihood distribution? 22:30 Example: Building models in PyMC 27:31 Stochastic and deterministic variables 37:11 Observed Random Variables 41:00 Question: To what extent are the features of PyMC supported if compiled in different backends? 41:47 Markov Chain Monte Carlo and Bayesian approximation 43:04 Markov chains 44;19 Reversible Markov chains 45:06 Metropolis sampling 48:00 Hamiltonian Monte Carlo 49:10 Hamiltonian dynamics 50:49 No U-turn Sampler (NUTS) 52:11 Question: How do you know the number of leap frog steps to take? 52:55 Example: Markov Chain Monte Carlo in PyMC 1:13:30 Divergences and how to deal with them 1:15:08 Bayesian Fraction of Missing Information 1:16:25 Potential Scale Reduction 1:17:57 Goodness of fit 1:22:40 Intuitive Bayes course 1:23:09 Question: Do bookmakers use PyMC or Bayesian methods? 1:23:53 Question: How does it work if you have different samplers for different variables? 1:25:09 Question: What route should one take in case of data with many discrete variables and many possible values? 1:25:39 Question: Is there a natural way to use PyMC over a cluster of CPUs?