Confidence Interval Calculation

Oct 13, 2025 by pynkocean.com 37 views

Introduction

In mathematical statistics, a confidence interval is a range of values within which a population parameter is likely to lie. It is a crucial concept in statistical inference, as it provides a way to quantify the uncertainty associated with a sample estimate. In this article, we will discuss how to compute a confidence interval for a Gaussian-weighted integral using numerical integration, specifically the Monte Carlo method.

The Problem

We are given the integral $I=\int_{-\infty}^\infty e^{-x^2}\vert \sin x\vert ~\mathrm dx.$ Our goal is to provide a $96\%$ confidence interval for this integral. To do this, we will use numerical integration to approximate the value of the integral and then apply the Monte Carlo method to estimate the standard error of the approximation.

Numerical Integration

Numerical integration is a method for approximating the value of a definite integral. There are several numerical integration methods available, including the Monte Carlo method, which we will use in this article. The Monte Carlo method is a stochastic method that uses random sampling to approximate the value of an integral.

The Monte Carlo Method

The Monte Carlo method is based on the idea of generating random points within a region and then using these points to estimate the value of an integral. In the case of the integral $I=\int_{-\infty}^\infty e^{-x^2}\vert \sin x\vert ~\mathrm dx,$ we can generate random points within the region $[-\infty, \infty]$ and then use these points to estimate the value of the integral.

Python Implementation

We can implement the Monte Carlo method in Python using the following code:

import numpy as np
def monte_carlo_integration(f, a, b, num_samples):
"""
Approximate the value of a definite integral using the Monte Carlo method.
Parameters:
f (function): The function to integrate.
a (float): The lower bound of the integral.
b (float): The upper bound of the integral.
num_samples (int): The number of random samples to generate.

Returns:
float: The approximate value of the integral.
&quot;&quot;&quot;
x = np.random.uniform(a, b, num_samples)
y = np.random.uniform(0, 1, num_samples)
z = np.sqrt(-2 * np.log(y)) * np.cos(np.pi * x)
return np.mean(z)

def f(x):
"""
The function to integrate.
Parameters:
x (float): The input value.

Returns:
float: The value of the function at x.
&quot;&quot;&quot;
return np.exp(-x**2) * np.abs(np.sin(x))

a = -10
b = 10
num_samples = 1000000
I_approx = monte_carlo_integration(f, a, b, num_samples)
print("Approximate value of the integral:", I_approx)

This code defines a function monte_carlo_integration that takes in a function f, the lower and upper bounds of the integral a and b, and the number of random samples to generate num_samples. The function generates random points within the region $[a, b]$ and then uses these points to estimate the value of the integral.

Confidence Interval

Once we have approximated the value of the integral, we can use the Monte Carlo method to estimate the standard error of the approximation. The standard error is a measure of the uncertainty associated with the approximation.

We can estimate the standard error using the following formula:

\mathrm{SE} = \frac{\sigma}{\sqrt{n}}

where $\sigma$ is the standard deviation of the approximation and $n$ is the number of random samples.

We can estimate the standard deviation using the following formula:

\sigma = \sqrt{\frac{1}{n-1} \sum_{i=1}^n (x_i - \bar{x})^2}

where $x_i$ is the $i$ th random sample and $\bar{x}$ is the mean of the random samples.

We can implement this in Python using the following code:

import numpy as np
def estimate_standard_error(I_approx, num_samples):
"""
Estimate the standard error of the approximation.
Parameters:
I_approx (float): The approximate value of the integral.
num_samples (int): The number of random samples.

Returns:
float: The estimated standard error.
&quot;&quot;&quot;
x = np.random.uniform(-10, 10, num_samples)
y = np.random.uniform(0, 1, num_samples)
z = np.sqrt(-2 * np.log(y)) * np.cos(np.pi * x)
std_dev = np.std(z)
return std_dev / np.sqrt(num_samples)

num_samples = 1000000
I_approx = monte_carlo_integration(f, -10, 10, num_samples)
std_err = estimate_standard_error(I_approx, num_samples)
print("Estimated standard error:", std_err)

This code defines a function estimate_standard_error that takes in the approximate value of the integral I_approx and the number of random samples num_samples. The function generates random points within the region $[-10, 10]$ and then uses these points to estimate the standard deviation of the approximation.

Confidence Interval Calculation

Once we have estimated the standard error, we can calculate the confidence interval using the following formula:

\mathrm{CI} = \left[I - z_{\alpha/2} \frac{\mathrm{SE}}{\sqrt{n}}, I + z_{\alpha/2} \frac{\mathrm{SE}}{\sqrt{n}}\right]

where $I$ is the approximate value of the integral, $z_{\alpha/2}$ is the critical value from the standard normal distribution, $\mathrm{SE}$ is the estimated standard error, and $n$ is the number of random samples.

We can implement this in Python using the following code:

import numpy as np
def calculate_confidence_interval(I_approx, std_err, num_samples, alpha):
"""
Calculate the confidence interval.
Parameters:
I_approx (float): The approximate value of the integral.
std_err (float): The estimated standard error.
num_samples (int): The number of random samples.
alpha (float): The significance level.

Returns:
tuple: The lower and upper bounds of the confidence interval.
&quot;&quot;&quot;
z_alpha_2 = np.sqrt(np.log(1 / (1 - alpha)))
return (I_approx - z_alpha_2 * std_err / np.sqrt(num_samples), I_approx + z_alpha_2 * std_err / np.sqrt(num_samples))

num_samples = 1000000
alpha = 0.04
I_approx = monte_carlo_integration(f, -10, 10, num_samples)
std_err = estimate_standard_error(I_approx, num_samples)
CI = calculate_confidence_interval(I_approx, std_err, num_samples, alpha)
print("Confidence interval:", CI)

This code defines a function calculate_confidence_interval that takes in the approximate value of the integral I_approx, the estimated standard error std_err, the number of random samples num_samples, and the significance level alpha. The function calculates the confidence interval using the formula above.

Conclusion

In this article, we discussed how to compute a confidence interval for a Gaussian-weighted integral using numerical integration and the Monte Carlo method. We implemented the Monte Carlo method in Python and estimated the standard error of the approximation. We then calculated the confidence interval using the estimated standard error and the significance level. The confidence interval provides a range of values within which the population parameter is likely to lie, and it is a crucial concept in statistical inference.

References

[1] Monte Carlo Methods in Statistical Physics, by K. Binder and D. W. Heermann
[2] Numerical Methods for Scientists and Engineers, by R. W. Hamming
[3] Confidence Intervals, by J. M. Bland and D. G. Altman

Appendix

The following is the Python code for the functions used in this article:

import numpy as np
def monte_carlo_integration(f, a, b, num_samples):
"""
Approximate the value of a definite integral using the Monte Carlo method.
Parameters:
f (function): The function to integrate.
a (float): The lower bound of the integral.
b (float): The upper bound of the integral.
num_samples (int): The number of random samples.

Returns:
float: The approximate value of the integral.
&quot;&quot;&quot;
x = np.random.uniform(a, b, num_samples)
y = np.random.uniform(0, 1, num_samples)
z = np.sqrt(-2 * np.log(y)) * np.cos(np.pi * x)
return np.mean(z)

def estimate_standard_error(I_approx, num_samples):
"""
Estimate the standard error of the approximation.
Parameters:
I_approx (float): The approximate value of the integral.
num_samples (int): The number of random samples.

Returns:
float: The estimated standard error.
&quot;&quot;&quot;
x = np.random.uniform(-10, 10, num_samples)
y = np.random.uniform(0, 1, num_samples)
z = np.sqrt(-2 * np.log(y)) * np.cos(np.pi * x)
std_dev = np.std(z)
return std_dev / np.sqrt(num_samples)


Q: What is a confidence interval, and why is it important?
A: A confidence interval is a range of values within which a population parameter is likely to lie. It is a crucial concept in statistical inference, as it provides a way to quantify the uncertainty associated with a sample estimate. In this article, we discussed how to compute a confidence interval for a Gaussian-weighted integral using numerical integration and the Monte Carlo method.
Q: What is the Monte Carlo method, and how does it work?
A: The Monte Carlo method is a stochastic method that uses random sampling to approximate the value of an integral. It works by generating random points within a region and then using these points to estimate the value of the integral. In the case of the integral  $I=\int_{-\infty}^\infty e^{-x^2}\vert \sin x\vert ~\mathrm dx,$  we can generate random points within the region  $[-\infty, \infty]$  and then use these points to estimate the value of the integral.
Q: How do you estimate the standard error of the approximation?
A: We can estimate the standard error using the following formula:
 $\mathrm{SE} = \frac{\sigma}{\sqrt{n}}$ 
where  $\sigma$  is the standard deviation of the approximation and  $n$  is the number of random samples. We can estimate the standard deviation using the following formula:
 $\sigma = \sqrt{\frac{1}{n-1} \sum_{i=1}^n (x_i - \bar{x})^2}$ 
where  $x_i$  is the  $i$ th random sample and  $\bar{x}$  is the mean of the random samples.
Q: How do you calculate the confidence interval?
A: We can calculate the confidence interval using the following formula:
 $\mathrm{CI} = \left[I - z_{\alpha/2} \frac{\mathrm{SE}}{\sqrt{n}}, I + z_{\alpha/2} \frac{\mathrm{SE}}{\sqrt{n}}\right]$ 
where  $I$  is the approximate value of the integral,  $z_{\alpha/2}$  is the critical value from the standard normal distribution,  $\mathrm{SE}$  is the estimated standard error, and  $n$  is the number of random samples.
Q: What is the significance level, and how does it affect the confidence interval?
A: The significance level is the probability of rejecting the null hypothesis when it is true. It is denoted by  $\alpha$  and is typically set to 0.05. The confidence interval is affected by the significance level, as a higher significance level will result in a wider confidence interval.
Q: How many random samples do you need to generate to get a good estimate of the integral?
A: The number of random samples needed to get a good estimate of the integral depends on the desired level of accuracy. A larger number of random samples will result in a more accurate estimate, but it will also increase the computational time.
Q: Can you use other numerical integration methods to estimate the integral?
A: Yes, there are several other numerical integration methods that can be used to estimate the integral, including the trapezoidal rule, Simpson's rule, and Gaussian quadrature. Each method has its own strengths and weaknesses, and the choice of method will depend on the specific problem being solved.
Q: How do you handle the case where the integral is not well-behaved?
A: If the integral is not well-behaved, it may be necessary to use a different numerical integration method or to transform the integral into a more tractable form. In some cases, it may be necessary to use a combination of different methods to get an accurate estimate of the integral.
Q: Can you use the Monte Carlo method to estimate the integral of a function that is not Gaussian-weighted?
A: Yes, the Monte Carlo method can be used to estimate the integral of a function that is not Gaussian-weighted. However, the method may not be as efficient as other methods, such as Gaussian quadrature, for certain types of functions.
Q: How do you implement the Monte Carlo method in Python?
A: The Monte Carlo method can be implemented in Python using the following code:
import numpy as np

def monte_carlo_integration(f, a, b, num_samples):
    &quot;&quot;&quot;
    Approximate the value of a definite integral using the Monte Carlo method.

    Parameters:
    f (function): The function to integrate.
    a (float): The lower bound of the integral.
    b (float): The upper bound of the integral.
    num_samples (int): The number of random samples.

    Returns:
    float: The approximate value of the integral.
    &quot;&quot;&quot;
    x = np.random.uniform(a, b, num_samples)
    y = np.random.uniform(0, 1, num_samples)
    z = np.sqrt(-2 * np.log(y)) * np.cos(np.pi * x)
    return np.mean(z)

def estimate_standard_error(I_approx, num_samples):
    &quot;&quot;&quot;
    Estimate the standard error of the approximation.

    Parameters:
    I_approx (float): The approximate value of the integral.
    num_samples (int): The number of random samples.

    Returns:
    float: The estimated standard error.
    &quot;&quot;&quot;
    x = np.random.uniform(-10, 10, num_samples)
    y = np.random.uniform(0, 1, num_samples)
    z = np.sqrt(-2 * np.log(y)) * np.cos(np.pi * x)
    std_dev = np.std(z)
    return std_dev / np.sqrt(num_samples)

def calculate_confidence_interval(I_approx, std_err, num_samples, alpha):
    &quot;&quot;&quot;
    Calculate the confidence interval.

    Parameters:
    I_approx (float): The approximate value of the integral.
    std_err (float): The estimated standard error.
    num_samples (int): The number of random samples.
    alpha (float): The significance level.

    Returns:
    tuple: The lower and upper bounds of the confidence interval.
    &quot;&quot;&quot;
    z_alpha_2 = np.sqrt(np.log(1 / (1 - alpha)))
    return (I_approx - z_alpha_2 * std_err / np.sqrt(num_samples), I_approx + z_alpha_2 * std_err / np.sqrt(num_samples))

num_samples = 1000000
alpha = 0.04

I_approx = monte_carlo_integration(f, -10, 10, num_samples)
std_err = estimate_standard_error(I_approx, num_samples)
CI = calculate_confidence_interval(I_approx, std_err, num_samples, alpha)
print(&quot;Confidence interval:&quot;, CI)
</code></pre>
<p>This code defines the functions <code>monte_carlo_integration</code>, <code>estimate_standard_error</code>, and <code>calculate_confidence_interval</code> that are used to estimate the integral and calculate the confidence interval.</p>


                
                    
                        

                    
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