问题类型1：参数估计
真实值是否等于X？
给出数据，对于参数，可能的值的概率分布是多少？
例子1：抛硬币问题
硬币扔了n次，正面朝上是h次。
参数问题
想知道 p 的可能性。给定 n 扔的次数和 h 正面朝上次数，p 的值很可能接近 0.5，比如说在 [0.48，0.52]？
说明
- 参数的先验信念：p∼Uniform(0,1)
- 似然函数：data∼Bernoulli(p)
import pymc3 as pm
import numpy.random as npr
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from collections import Counter
import seaborn as snssns.set_style('white')
sns.set_context('poster')%load_ext autoreload
%autoreload 2
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import warnings
warnings.filterwarnings('ignore')
from random import shuffle
total = 30
n_heads = 11
n_tails = total - n_heads
tosses = [1] * n_heads + [0] * n_tails
shuffle(tosses)可视化数据：
def plot_coins():fig = plt.figure()ax = fig.add_subplot(1,1,1)ax.bar(list(Counter(tosses).keys()), list(Counter(tosses).values()))ax.set_xticks([0, 1])ax.set_xticklabels(['tails', 'heads'])ax.set_ylim(0, 20)ax.set_yticks(np.arange(0, 21, 5)) return figfig = plot_coins()
plt.show()建立模型：
with pm.Model() as coin_model:# Specify prior using Uniform object.p_prior = pm.Uniform('p', 0, 1) # Specify likelihood using Bernoulli object.like = pm.Bernoulli('likelihood', p=p_prior, observed=tosses) # "observed=data" is key for likelihood.MCMC Inference Button
with coin_model:step = pm.Metropolis() # focus on this, the Inference Button:coin_trace = pm.sample(2000, step=step)结果：
pm.traceplot(coin_trace)
plt.show()pm.plot_posterior(coin_trace[100:], color='#87ceeb',rope=[0.48,0.52], point_estimate='mean', ref_val=0.5)
plt.show()
例子2：化学活性问题
我有一个新开发的分子X； X在阻止流感方面的效果有多好？
实验
- 测试X的浓度范围，测量流感活动
- 根据实验结果计算 IC50：导致病毒复制率减半的X浓度。
数据
import numpy as np
import pandas as pdchem_data = [(0.00080, 99),
(0.00800, 91),
(0.08000, 89),
(0.40000, 89),
(0.80000, 79),
(1.60000, 61),
(4.00000, 39),
(8.00000, 25),
(80.00000, 4)]chem_df = pd.DataFrame(chem_data)
chem_df.columns = ['concentration', 'activity']
chem_df['concentration_log'] = chem_df['concentration'].apply(lambda x:np.log10(x)) 参数问题
给出数据，化学品的IC50 值是多少, 以及其周围的不确定性？
说明
可视化数据
def plot_chemical_data(log=True):fig = plt.figure(figsize=(10,6))ax = fig.add_subplot(1,1,1) if log:ax.scatter(x=chem_df['concentration_log'], y=chem_df['activity'])ax.set_xlabel('log10(concentration (mM))', fontsize=20) else:ax.scatter(x=chem_df['concentration'], y=chem_df['activity'])ax.set_xlabel('concentration (mM)', fontsize=20)ax.set_xticklabels([int(i) for i in ax.get_xticks()], fontsize=18)ax.set_yticklabels([int(i) for i in ax.get_yticks()], fontsize=18)plt.hlines(y=50, xmin=min(ax.get_xlim()), xmax=max(ax.get_xlim()), linestyles='--',) return figfig = plot_chemical_data(log=True)
plt.show()with pm.Model() as ic50_model:beta = pm.HalfNormal('beta', sd=100**2)ic50_log10 = pm.Flat('IC50_log10') # Flat prior# MATH WITH DISTRIBUTION OBJECTS!measurements = beta / (1 + np.exp(chem_df['concentration_log'].values - ic50_log10))y_like = pm.Normal('y_like', mu=measurements, observed=chem_df['activity']) ic50 = pm.Deterministic('IC50', np.power(10, ic50_log10))# MCMC Inference Button
with ic50_model:step = pm.Metropolis()ic50_trace = pm.sample(10000, step=step)pm.traceplot(ic50_trace[2000:], varnames=['IC50_log10', 'IC50']) # live: sample from step 2000 onwards.
plt.show()
pm.plot_posterior(ic50_trace[4000:], varnames=['IC50'], color='#87ceeb', point_estimate='mean')
plt.show()该化学物质的IC50在约 [2mM，2.4mM]（95％HPD）
问题类型2：实验组之间的比较
实验组和对照组的不同
例子1：药物IQ问题
药物治疗是否影响 IQ Scores
数据（包括对照实验数据）
drug = [ 99., 110., 107., 104., 省略]
placebo = [ 95., 105., 103., 99., 省略]def ECDF(data):x = np.sort(data)y = np.cumsum(x) / np.sum(x) return x, ydef plot_drug():fig = plt.figure()ax = fig.add_subplot(1,1,1)x_drug, y_drug = ECDF(drug)ax.plot(x_drug, y_drug, label='drug, n={0}'.format(len(drug)))x_placebo, y_placebo = ECDF(placebo)ax.plot(x_placebo, y_placebo, label='placebo, n={0}'.format(len(placebo)))ax.legend()ax.set_xlabel('IQ Score')ax.set_ylabel('Cumulative Frequency')ax.hlines(0.5, ax.get_xlim()[0], ax.get_xlim()[1], linestyle='--') return figfig = plot_drug()
plt.show()
from scipy.stats import ttest_indttest_ind(drug, placebo)# Ttest_indResult(statistic=2.2806701634329549, pvalue=0.025011500508647616)
实验
- 随机将参与者分配给两个实验组：
- +drug vs. -drug
- 测量每个参与者的 IQ Scores
说明
建模：
y_vals = np.concatenate([drug, placebo])
labels = ['drug'] * len(drug) + ['placebo'] * len(placebo)data = pd.DataFrame([y_vals, labels]).T
data.columns = ['IQ', 'treatment']
with pm.Model() as kruschke_model: # Linking Distribution Objects together is done by # passing objects into other objects' parameters.mu_drug = pm.Normal('mu_drug', mu=0, sd=100**2)mu_placebo = pm.Normal('mu_placebo', mu=0, sd=100**2)sigma_drug = pm.HalfCauchy('sigma_drug', beta=100)sigma_placebo = pm.HalfCauchy('sigma_placebo', beta=100)nu = pm.Exponential('nu', lam=1/29) + 1drug_like = pm.StudentT('drug', nu=nu, mu=mu_drug, sd=sigma_drug, observed=drug)placebo_like = pm.StudentT('placebo', nu=nu, mu=mu_placebo, sd=sigma_placebo, observed=placebo)diff_means = pm.Deterministic('diff_means', mu_drug - mu_placebo)pooled_sd = pm.Deterministic('pooled_sd', np.sqrt(np.power(sigma_drug, 2) + np.power(sigma_placebo, 2) / 2))effect_size = pm.Deterministic('effect_size', diff_means / pooled_sd)with kruschke_model:kruschke_trace = pm.sample(10000, step=pm.Metropolis())结果：
pm.traceplot(kruschke_trace[2000:], varnames=['mu_drug', 'mu_placebo'])
plt.show()
pm.plot_posterior(kruschke_trace[2000:], color='#87ceeb',varnames=['mu_drug', 'mu_placebo', 'diff_means'])
plt.show()
)
