PyMC Python中的贝叶斯建模与概率编程

PyMC简介

PyMC，Python第三方库，可用于贝叶斯建模与概率编程，专注于高级马尔可夫链蒙特卡罗（MCMC）和变分推理（VI）算法。

PyMC应用实例

import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc as pm
import xarray as xr

from pymc import HalfCauchy, Model, Normal, sample
RANDOM_SEED = 8927
rng = np.random.default_rng(RANDOM_SEED)

%config InlineBackend.figure_format = 'retina'
az.style.use("arviz-darkgrid")
size = 200
true_intercept = 1
true_slope = 2

x = np.linspace(0, 1, size)
# y = a + b*x
true_regression_line = true_intercept + true_slope * x
# add noise
y = true_regression_line + rng.normal(scale=0.5, size=size)

data = pd.DataFrame(dict(x=x, y=y))
fig = plt.figure(figsize=(7, 7))
ax = fig.add_subplot(111, xlabel="x", ylabel="y", title="Generated data and underlying model")
ax.plot(x, y, "x", label="sampled data")
ax.plot(x, true_regression_line, label="true regression line", lw=2.0)
plt.legend(loc=0);

PyMC Github统计数据

Apache License, Version 2.0

Github 7.9k stars

PyMC安装命令

conda create -c conda-forge -n pymc_env "pymc>=5"

提示：其中 pymc_env是虚拟环境的名称，可以自定义。

免责声明：内容编辑自网络，仅供参考，不保证正确性，不作任何决策依据！！以上数据皆截止于博文的写稿日期。