Saran Ahluwalia ahlusar1989

generator	title	viewport
pandoc	Gibbs sampling	width=device-width, initial-scale=1

::: {.container-fluid .main-container} ::: {#header .fluid-row} Gibbs sampling {#gibbs-sampling .title .toc-ignore}

	"""
	Plot white noise with added sinusoidal signal
	"""
	import numpy as np
	import matplotlib.pyplot as plt
	import sys
	import h5py

	fs = 4096 # sampling rate [Hz]
	T = 4 # duration [s]

	#' Hellwig's method for choosing subset of independet variables
	#'
	#' Hellwig's method selects a subset of independent variables in a linear
	#' regression model based on their correlations with some dependent variable as
	#' well as correlations between themselves. The goal is to select a subset of
	#' variables which are fairly independent from each other but highly correlated
	#' with the dependent variable.
	#'
	#' Given \eqn{m} independent variables Hellwig's method consists of evaluating
	#' all \eqn{2^m - 1} combinations using the following steps:

	---
	generator: pandoc
	title: Gibbs sampling
	viewport: 'width=device-width, initial-scale=1'
	---

	::: {.container-fluid .main-container}
	::: {#header .fluid-row}
	Gibbs sampling {#gibbs-sampling .title .toc-ignore}
	==============

	#!/usr/bin/env python
	# -- coding: UTF-8 --
	import warnings
	import numpy as np
	import pandas as pd
	import sys

	__author__ = "Mohsen Mesgarpour"
	__copyright__ = "Copyright 2016, https://github.com/mesgarpour"
	__credits__ = ["Mohsen Mesgarpour"]