id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_tfvl7qzf7jgyhlqbehmj5muepi	John Salvatier	Probabilistic programming in Python using PyMC3	2016	24	.pdf	application/pdf	8110	1128	64	probabilistic programming languages, PyMC3 allows model specification directly in use custom statistical distributions, samplers and transformation functions, as required by Bayesian linear regression model with normal priors on the parameters. We can simulate some data from this model using NumPy's random module, and then use from pymc3 import Model, Normal, HalfNormal Detailed notes about distributions, sampling methods and other PyMC3 functions are operators and functions to PyMC3 objects results in tremendous model expressivity. stochastic random variables or models with highly non-normal posterior distributions. from pymc3 import NUTS, sample Figure 2 Kernel density estimates and simulated trace for each variable in the linear regression model. points for variables at the model specification stage, it is possible to provide an initial value Figure 4 Posterior samples of degrees of freedom (nu) and scale (sigma) parameters of the stochastic volatility model. Figure 7 Posterior distributions and traces from disasters change point model.	./cache/work_tfvl7qzf7jgyhlqbehmj5muepi.pdf	./txt/work_tfvl7qzf7jgyhlqbehmj5muepi.txt