bc bclow

## gist:2e00e51a234ba0829b45

      
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                bclow
                / gist:2e00e51a234ba0829b45
            
            
              Created
              February 27, 2016 17:52
                — forked from debasishg/gist:8172796
            
              
                A collection of links for streaming algorithms and data structures
              
          
General Background and Overview


Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
Models and Issues in Data Stream Systems
Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
[Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&amp;rep


## gist:c1c855d425213bec3e28
import pymc as pm

# The parameters are the bounds of the Uniform.
p = pm.Uniform('p', lower=0, upper=1)

# set constants
p_true = 0.05  # remember, this is unknown.
N = 1500

# sample N Bernoulli random variables from Ber(0.05).
	import pymc as pm

	# The parameters are the bounds of the Uniform.
	p = pm.Uniform('p', lower=0, upper=1)

	# set constants
	p_true = 0.05 # remember, this is unknown.
	N = 1500

	# sample N Bernoulli random variables from Ber(0.05).