Skip to content

Wrong choice of quantiles for CI? #116

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Aug 8, 2013
Merged

Wrong choice of quantiles for CI? #116

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
8 changes: 4 additions & 4 deletions Chapter2_MorePyMC/MorePyMC.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -709,7 +709,7 @@
"source": [
"Had we had stronger beliefs, we could have expressed them in the prior above.\n",
"\n",
"For this example, consider $p_A = 0.05$, and $N = 1500$ users shown site A, and we will simulate whether the user made a purchase or not. To simulate this from $N$ trials, we will use a *Bernoulli* distribution: if $ X\\ \\sim \\text{Ber}(p)$, then $X$ is 1 with probability $p$ and 0 with probability $1-p$. Of course, in practice we do not know $p_A$, but we will use it here to simulate the data."
"For this example, consider $p_A = 0.05$, and $N = 1500$ users shown site A, and we will simulate whether the user made a purchase or not. To simulate this from $N$ trials, we will use a *Bernoulli* distribution: if $ X\\ \\sim \\text{Ber}(p)$, then $X$ is 1 with probability $p$ and 0 with probability $1$. Of course, in practice we do not know $p_A$, but we will use it here to simulate the data."
]
},
{
Expand Down Expand Up @@ -1819,7 +1819,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"A Normal random variable can take on any real value, but the variable is very likely to be relatively close to $\\mu$. In fact, the expected value of a Normal is equal to its $\\mu$ parameter:\n",
"A Normal random variable can be take on any real number, but the variable is very likely to be relatively close to $\\mu$. In fact, the expected value of a Normal is equal to its $\\mu$ parameter:\n",
"\n",
"$$ E[ X | \\mu, \\tau] = \\mu$$\n",
"\n",
Expand Down Expand Up @@ -2048,8 +2048,8 @@
"input": [
"from scipy.stats.mstats import mquantiles\n",
"\n",
"# vectorized bottom and top 5% quantiles for \"confidence interval\"\n",
"qs = mquantiles(p_t, [0.05, 0.95], axis=0)\n",
"# vectorized bottom and top 2.5% quantiles for \"confidence interval\"\n",
"qs = mquantiles(p_t, [0.025, 0.975], axis=0)\n",
"plt.fill_between(t[:, 0], *qs, alpha=0.7,\n",
" color=\"#7A68A6\")\n",
"\n",
Expand Down