Bayesian MCMC model and Bayesian regression analysis model

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技术栈
python
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未确定任何仓库文件、测试或入口点。请从已发布的 TensorFlow Probability 模型入手，尤其是 JointDistributionCoroutine 和 sample_chain，并明确该 issue 需要概念性文档还是可运行示例。完成标准是进行一次范围明确的文档修改，解释这两个概念之间的关系。

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描述

I wonder the difference between Bayesian MCMC model and Bayesian regression analysis model.
The tensorflow_probability library is being used. The code has been imported from Excel into the target value and various related values, and we are creating a model that predicts the target value only with related values and sets residuals when comparing actual data and predicted values to examine errors.

  • The libraries used in this process are.

import tensorflow as tf
import tensorflow_probability as tfp
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

tfd = tfp.distributions
tfb = tfp.bijectors

  • Defining the reliability (pre-probability) of the true value
    sensor_accuracy = 0.90
    prior_loc = tf.cast(actual_value, dtype=tf.float32)
    prior_scale = tf.cast((1 - sensor_accuracy) * actual_value, dtype=tf.float32)

  • After defining the model

joint = tfd.JointDistributionCoroutine(model)
num_results = 10000
num_burnin_steps = 500

def target_log_prob_fn(predicted_value):
return joint.log_prob(predicted_value, n)

initial_state = [tf.constant(actual_value, dtype=tf.float32)]

hmc = tfp.mcmc.HamiltonianMonteCarlo(
target_log_prob_fn=target_log_prob_fn,
num_leapfrog_steps=20,
step_size=0.01)

@tf.function
def run_chain():
return tfp.mcmc.sample_chain(
num_results=num_results,
num_burnin_steps=num_burnin_steps,
current_state=initial_state,
kernel=hmc,
trace_fn=lambda current_state, kernel_results: kernel_results)

samples, kernel_results = run_chain()

I started coding for the first time, so I got advice from people around me, and also used chat gpt to construct the code. As a result, I am asking a question because I do not understand clearly whether the concept that made up this model is Bayesian MCMC or Bayesian regression analysis due to the lack of understanding of the code overall.
Thank you for reading. Please give me a lot of advice.

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