Mixing
What's wrong with my simulation of the Central Limit Theorem?
$begingroup$
While there is code in this question, I suspect the answer will be mathematical.
I am trying to create a numerical simulation of the Central Limit Theorem (CLT). My understanding is that if $S_n = frac{X_1 + X_2 + dots + X_n}{n}$ for $n$ i.i.d. random variables ${X_1, X_2, dots X_n}$, then the CLT states that
$$
lim_{n rightarrow infty} sqrt{n}(S_n - mu) stackrel{text{dist}}{rightarrow} mathcal{N}(0, sigma^2)
$$
where $X_i$ has expectation $mu$ and variance $sigma^2$. So one way to empirically explore this is to plot both the distribution $mathcal{N}(0, sigma^2)$ and the distribution from $n$ random samples of the random variable $sqrt{n}(S_n - mu)$. When I do that, I get this figure:
My code is below. In my simulation, $X_i sim text{Uniform}(0, 1)$, meaning that $mu = frac{1}{2}(0 - 1)$ and $sigma^2 = frac{1}{12}(1 - 0)$. I first generate 2000 replicates of $sqrt{n} (S_n - mu)$ and plot that distribution. I then plot the theoretical distribution that I expect this distribution to converge to, namely $mathcal{N}(0, sigma^2)$. Unfortunately, the two distributions look way off.
I suspect I have a "mathematical bug" rather than an programming one. In summary: What is wrong with my thinking about the CLT that makes this simulation wrong?
Simulation code
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
from scipy.stats import norm as normal
a = 0
b = 1
n = 100
reps = 2000
mu_lim = 1/2. * (a + b)
var_lim = 1/12. * (b - a)**2
rvs =
for _ in range(reps):
Sn = np.random.uniform(a, b, n).sum() / n
rv = np.sqrt(n) * (Sn - mu_lim)
rvs.append(rv)
_, bins, _ = plt.hist(rvs, bins=50, density=True)
(mu_actual, var_actual) = normal.fit(rvs)
y = normal.pdf(bins, mu_actual, var_actual)
plt.plot(bins, y, 'r--', label='Actual N(., .) for n = %s' % n)
# Plot distribution to converge to
x = np.linspace(-3*var_lim, 3*var_lim, 100)
plt.plot(x, normal.pdf(x, 0, var_lim), label='N(0, s^2)')
plt.legend()
plt.show()
probability central-limit-theorem
asked Jan 25 at 22:50
gwggwg
1,00711023
$endgroup$
add a comment |
$begingroup$
While there is code in this question, I suspect the answer will be mathematical.
I am trying to create a numerical simulation of the Central Limit Theorem (CLT). My understanding is that if $S_n = frac{X_1 + X_2 + dots + X_n}{n}$ for $n$ i.i.d. random variables ${X_1, X_2, dots X_n}$, then the CLT states that
$$
lim_{n rightarrow infty} sqrt{n}(S_n - mu) stackrel{text{dist}}{rightarrow} mathcal{N}(0, sigma^2)
$$
where $X_i$ has expectation $mu$ and variance $sigma^2$. So one way to empirically explore this is to plot both the distribution $mathcal{N}(0, sigma^2)$ and the distribution from $n$ random samples of the random variable $sqrt{n}(S_n - mu)$. When I do that, I get this figure:
My code is below. In my simulation, $X_i sim text{Uniform}(0, 1)$, meaning that $mu = frac{1}{2}(0 - 1)$ and $sigma^2 = frac{1}{12}(1 - 0)$. I first generate 2000 replicates of $sqrt{n} (S_n - mu)$ and plot that distribution. I then plot the theoretical distribution that I expect this distribution to converge to, namely $mathcal{N}(0, sigma^2)$. Unfortunately, the two distributions look way off.
I suspect I have a "mathematical bug" rather than an programming one. In summary: What is wrong with my thinking about the CLT that makes this simulation wrong?
Simulation code
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
from scipy.stats import norm as normal
a = 0
b = 1
n = 100
reps = 2000
mu_lim = 1/2. * (a + b)
var_lim = 1/12. * (b - a)**2
rvs =
for _ in range(reps):
Sn = np.random.uniform(a, b, n).sum() / n
rv = np.sqrt(n) * (Sn - mu_lim)
rvs.append(rv)
_, bins, _ = plt.hist(rvs, bins=50, density=True)
(mu_actual, var_actual) = normal.fit(rvs)
y = normal.pdf(bins, mu_actual, var_actual)
plt.plot(bins, y, 'r--', label='Actual N(., .) for n = %s' % n)
# Plot distribution to converge to
x = np.linspace(-3*var_lim, 3*var_lim, 100)
plt.plot(x, normal.pdf(x, 0, var_lim), label='N(0, s^2)')
plt.legend()
plt.show()
probability central-limit-theorem
asked Jan 25 at 22:50
gwggwg
1,00711023
$endgroup$
2
$begingroup$
The plot of the normal density is wrong. The density of $N(0,sigma^2)$ is maximal at $0$, where it equals $1/sqrt{2pisigma^2}$. Thus, if $sigma^2=1/12$, the maximum of the PDF should be $sqrt{6/pi}approx1.38$, not your value almost at $5$. Indeed, your empirical PDF peaks at about $1.5$, close to $sqrt{6/pi}$.
$endgroup$
– Did
Jan 25 at 23:29
add a comment |
$begingroup$
While there is code in this question, I suspect the answer will be mathematical.
I am trying to create a numerical simulation of the Central Limit Theorem (CLT). My understanding is that if $S_n = frac{X_1 + X_2 + dots + X_n}{n}$ for $n$ i.i.d. random variables ${X_1, X_2, dots X_n}$, then the CLT states that
$$
lim_{n rightarrow infty} sqrt{n}(S_n - mu) stackrel{text{dist}}{rightarrow} mathcal{N}(0, sigma^2)
$$
where $X_i$ has expectation $mu$ and variance $sigma^2$. So one way to empirically explore this is to plot both the distribution $mathcal{N}(0, sigma^2)$ and the distribution from $n$ random samples of the random variable $sqrt{n}(S_n - mu)$. When I do that, I get this figure:
My code is below. In my simulation, $X_i sim text{Uniform}(0, 1)$, meaning that $mu = frac{1}{2}(0 - 1)$ and $sigma^2 = frac{1}{12}(1 - 0)$. I first generate 2000 replicates of $sqrt{n} (S_n - mu)$ and plot that distribution. I then plot the theoretical distribution that I expect this distribution to converge to, namely $mathcal{N}(0, sigma^2)$. Unfortunately, the two distributions look way off.
I suspect I have a "mathematical bug" rather than an programming one. In summary: What is wrong with my thinking about the CLT that makes this simulation wrong?
Simulation code
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
from scipy.stats import norm as normal
a = 0
b = 1
n = 100
reps = 2000
mu_lim = 1/2. * (a + b)
var_lim = 1/12. * (b - a)**2
rvs =
for _ in range(reps):
Sn = np.random.uniform(a, b, n).sum() / n
rv = np.sqrt(n) * (Sn - mu_lim)
rvs.append(rv)
_, bins, _ = plt.hist(rvs, bins=50, density=True)
(mu_actual, var_actual) = normal.fit(rvs)
y = normal.pdf(bins, mu_actual, var_actual)
plt.plot(bins, y, 'r--', label='Actual N(., .) for n = %s' % n)
# Plot distribution to converge to
x = np.linspace(-3*var_lim, 3*var_lim, 100)
plt.plot(x, normal.pdf(x, 0, var_lim), label='N(0, s^2)')
plt.legend()
plt.show()
probability central-limit-theorem
asked Jan 25 at 22:50
gwggwg
1,00711023
$endgroup$
While there is code in this question, I suspect the answer will be mathematical.
I am trying to create a numerical simulation of the Central Limit Theorem (CLT). My understanding is that if $S_n = frac{X_1 + X_2 + dots + X_n}{n}$ for $n$ i.i.d. random variables ${X_1, X_2, dots X_n}$, then the CLT states that
$$
lim_{n rightarrow infty} sqrt{n}(S_n - mu) stackrel{text{dist}}{rightarrow} mathcal{N}(0, sigma^2)
$$
where $X_i$ has expectation $mu$ and variance $sigma^2$. So one way to empirically explore this is to plot both the distribution $mathcal{N}(0, sigma^2)$ and the distribution from $n$ random samples of the random variable $sqrt{n}(S_n - mu)$. When I do that, I get this figure:
My code is below. In my simulation, $X_i sim text{Uniform}(0, 1)$, meaning that $mu = frac{1}{2}(0 - 1)$ and $sigma^2 = frac{1}{12}(1 - 0)$. I first generate 2000 replicates of $sqrt{n} (S_n - mu)$ and plot that distribution. I then plot the theoretical distribution that I expect this distribution to converge to, namely $mathcal{N}(0, sigma^2)$. Unfortunately, the two distributions look way off.
I suspect I have a "mathematical bug" rather than an programming one. In summary: What is wrong with my thinking about the CLT that makes this simulation wrong?
Simulation code
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
from scipy.stats import norm as normal
a = 0
b = 1
n = 100
reps = 2000
mu_lim = 1/2. * (a + b)
var_lim = 1/12. * (b - a)**2
rvs =
for _ in range(reps):
Sn = np.random.uniform(a, b, n).sum() / n
rv = np.sqrt(n) * (Sn - mu_lim)
rvs.append(rv)
_, bins, _ = plt.hist(rvs, bins=50, density=True)
(mu_actual, var_actual) = normal.fit(rvs)
y = normal.pdf(bins, mu_actual, var_actual)
plt.plot(bins, y, 'r--', label='Actual N(., .) for n = %s' % n)
# Plot distribution to converge to
x = np.linspace(-3*var_lim, 3*var_lim, 100)
plt.plot(x, normal.pdf(x, 0, var_lim), label='N(0, s^2)')
plt.legend()
plt.show()
probability central-limit-theorem
probability central-limit-theorem
asked Jan 25 at 22:50
gwggwg
1,00711023
asked Jan 25 at 22:50
gwggwg
1,00711023
asked Jan 25 at 22:50
gwggwg
1,00711023
asked Jan 25 at 22:50
gwggwg
1,00711023
asked Jan 25 at 22:50
gwggwg
1,00711023
1,00711023
2
$begingroup$
The plot of the normal density is wrong. The density of $N(0,sigma^2)$ is maximal at $0$, where it equals $1/sqrt{2pisigma^2}$. Thus, if $sigma^2=1/12$, the maximum of the PDF should be $sqrt{6/pi}approx1.38$, not your value almost at $5$. Indeed, your empirical PDF peaks at about $1.5$, close to $sqrt{6/pi}$.
$endgroup$
– Did
Jan 25 at 23:29
add a comment |
2
$begingroup$
The plot of the normal density is wrong. The density of $N(0,sigma^2)$ is maximal at $0$, where it equals $1/sqrt{2pisigma^2}$. Thus, if $sigma^2=1/12$, the maximum of the PDF should be $sqrt{6/pi}approx1.38$, not your value almost at $5$. Indeed, your empirical PDF peaks at about $1.5$, close to $sqrt{6/pi}$.
$endgroup$
– Did
Jan 25 at 23:29
2
2
$begingroup$
The plot of the normal density is wrong. The density of $N(0,sigma^2)$ is maximal at $0$, where it equals $1/sqrt{2pisigma^2}$. Thus, if $sigma^2=1/12$, the maximum of the PDF should be $sqrt{6/pi}approx1.38$, not your value almost at $5$. Indeed, your empirical PDF peaks at about $1.5$, close to $sqrt{6/pi}$.
$endgroup$
– Did
Jan 25 at 23:29
$begingroup$
The plot of the normal density is wrong. The density of $N(0,sigma^2)$ is maximal at $0$, where it equals $1/sqrt{2pisigma^2}$. Thus, if $sigma^2=1/12$, the maximum of the PDF should be $sqrt{6/pi}approx1.38$, not your value almost at $5$. Indeed, your empirical PDF peaks at about $1.5$, close to $sqrt{6/pi}$.
$endgroup$
– Did
Jan 25 at 23:29
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
The norm.pdf function names the parameters “loc” and “scale”, and “scale” strongly suggests it should take the standard deviation, whereas you have passed in the variance. Per Did’s comment, it makes sense that you see a peak $sqrt{12}$ times too high, around 4.78 rather than 1.38.
answered Jan 26 at 0:12
spaceisdarkgreenspaceisdarkgreen
33.5k21753
$endgroup$
add a comment |
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$begingroup$
The norm.pdf function names the parameters “loc” and “scale”, and “scale” strongly suggests it should take the standard deviation, whereas you have passed in the variance. Per Did’s comment, it makes sense that you see a peak $sqrt{12}$ times too high, around 4.78 rather than 1.38.
answered Jan 26 at 0:12
spaceisdarkgreenspaceisdarkgreen
33.5k21753
$endgroup$
add a comment |
$begingroup$
The norm.pdf function names the parameters “loc” and “scale”, and “scale” strongly suggests it should take the standard deviation, whereas you have passed in the variance. Per Did’s comment, it makes sense that you see a peak $sqrt{12}$ times too high, around 4.78 rather than 1.38.
answered Jan 26 at 0:12
spaceisdarkgreenspaceisdarkgreen
33.5k21753
$endgroup$
add a comment |
$begingroup$
The norm.pdf function names the parameters “loc” and “scale”, and “scale” strongly suggests it should take the standard deviation, whereas you have passed in the variance. Per Did’s comment, it makes sense that you see a peak $sqrt{12}$ times too high, around 4.78 rather than 1.38.
answered Jan 26 at 0:12
spaceisdarkgreenspaceisdarkgreen
33.5k21753
$endgroup$
The norm.pdf function names the parameters “loc” and “scale”, and “scale” strongly suggests it should take the standard deviation, whereas you have passed in the variance. Per Did’s comment, it makes sense that you see a peak $sqrt{12}$ times too high, around 4.78 rather than 1.38.
answered Jan 26 at 0:12
spaceisdarkgreenspaceisdarkgreen
33.5k21753
answered Jan 26 at 0:12
spaceisdarkgreenspaceisdarkgreen
33.5k21753
answered Jan 26 at 0:12
spaceisdarkgreenspaceisdarkgreen
33.5k21753
answered Jan 26 at 0:12
spaceisdarkgreenspaceisdarkgreen
33.5k21753
33.5k21753
add a comment |
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$begingroup$
The plot of the normal density is wrong. The density of $N(0,sigma^2)$ is maximal at $0$, where it equals $1/sqrt{2pisigma^2}$. Thus, if $sigma^2=1/12$, the maximum of the PDF should be $sqrt{6/pi}approx1.38$, not your value almost at $5$. Indeed, your empirical PDF peaks at about $1.5$, close to $sqrt{6/pi}$.
$endgroup$
– Did
Jan 25 at 23:29