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# jet08013/BDA

63d2bb8 Apr 13, 2018
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 # coding: utf-8 # #### Problem 3.10.8 # # Analysis of proportions: a survey was done of bicycle and # other vehicular traffic in the neighborhood of the campus of the # University of California, Berkeley, in the spring of 1993. # Sixty city blocks were selected at random; each block was observed # for one hour, and the numbers of bicycles and other vehicles traveling # along that block were recorded. The sampling was stratified into six # types of city blocks: busy, fairly busy, and residential streets, with # and without bike routes, with ten blocks measured in each stratum. # Table 3.3 displays the number of bicycles and other vehicles # recorded in the study. For this problem, restrict your attention # to the first four rows of the table: the data on residential streets. # # (a) Let $y_1$ , . . . , $y_{10}$ and $z_1$ , . . . , $z_8$ be the # observed proportion of traffic that was on bicycles in the residential # streets with bike lanes and with no bike lanes, respectively # (so $y_1 = 16/(16 + 58)$ and $z_1 = 12/(12 + 113)$, for example). # Set up a model so that the $y_i$ ’s are independent and identically # distributed given parameters $\theta_y$ and the $z_i$ ’s are # independent and identically distributed given parameters $\theta_z$ . # # (b) Set up a prior distribution that is independent in # $\theta_y$ and $\theta_z$ . # # (c) Determine the posterior distribution for the parameters # in your model and draw 1000 simulations from the posterior distribution. # (Hint: $\theta_y$ and $\theta_z$ are independent in the posterior # distribution, so they can be simulated independently.) # # (d) Let $\mu_y = E(y_i |\theta_y )$ be the mean of the distribution # of the $y_i$ ’s; $\mu_y$ will be a function of $\theta_y$. # Similarly, define $\mu_z$ . Using your posterior simulations from (c), # plot a histogram of the posterior simulations of $\mu_y-\mu_z$, the # expected difference in proportions in bicycle traffic on residential # streets with and without bike lanes. We return to this example in # Exercise 5.13. # # Gelman, Andrew; Carlin, John B.; Stern, Hal S.; Dunson, David B.; # Vehtari, Aki; Rubin, Donald B.. Bayesian Data Analysis, # Third Edition (Chapman & Hall/CRC Texts in Statistical Science) (Page 81). # CRC Press. Kindle Edition. # # #### Data # |Type |Bike lane? |Counts of Bikes/others| # |--- |----------|----| # |Residential |yes |16/58, 9/90, 10/48, 13/57, 19/103, 20/57, 18/86, 17/112, 35/273, 55/64 | # |Residential |no |12/113, 1/18, 2/14, 4/44, 9/208, 7/67, 9/29, 8/154| # # Gelman, Andrew; Carlin, John B.; Stern, Hal S.; # Dunson, David B.; Vehtari, Aki; Rubin, Donald B.. # Bayesian Data Analysis, Third Edition ( # Chapman & Hall/CRC Texts in Statistical Science) # (Page 81). CRC Press. Kindle Edition. # #### Probably best to first do 3.10.6 # For that problem see the reference # [Raftery, 1988](https://www.stat.washington.edu/raftery/Research/PDF/bka1988.pdf) import pystan import numpy as np import matplotlib.pyplot as plt stan_code=""" data { int N; int bikes[N]; int others[N]; } parameters { real theta_b; real theta_v; } model { theta_b~uniform(0,100); theta_v~uniform(0,100); bikes~poisson(theta_b); others~poisson(theta_v); } generated quantities { real b_ppc; real o_ppc; real p ; o_ppc=poisson_rng(theta_v); b_ppc=poisson_rng(theta_b); p=o_ppc/(o_ppc+b_ppc); } """ sm=pystan.StanModel(model_code=stan_code) fit=sm.sampling(data=dict({'N':10,'bikes':[16,9,10,13,19,20,18,17,35,55],'others':[58, 90, 48, 57, 103, 57, 86, 112, 273, 64] })) print(fit.extract()) print(len(fit.extract()['b_ppc'])) fig,ax=plt.subplots(1,1) ax.hist(fit.extract()['b_ppc'],density=True) #ax[1].hist(bikes,density=True) plt.show()
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