View Manatee Proportions of Mortality.ipynb
1 2 3 4 5 6 7 8 9 10
{
"metadata": {
"name": "",
"signature": "sha256:158f8b01b663430159d4227a372122645c1c8ba515963b466bc49ade1164e141"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
View Dataset Statistics.ipynb
1 2 3 4 5 6 7 8 9 10
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
View missing_truncated.py
1 2 3 4 5 6 7 8 9
import pymc as pm
import numpy as np
missing_fill_value = 16.172
data = np.array([None, None, None, 12, 17, 20])
#data = np.where(data == np.array(None), missing_fill_value, data)
#masked_values = np.ma.masked_equal(data, value=missing_fill_value)
masked_values = np.ma.masked_array(data, np.equal(data, None), fill_value=10)
View Infantile Hemangioma Meta-analysis.ipynb
1 2 3 4 5 6 7 8 9 10
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Infantile Hemangioma Meta-analysis"
]
},
{
View Spatial Measles Model.ipynb
1 2 3 4 5 6 7 8 9 10
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Disease Outbreak Response Decision-making Under Uncertainty: A retrospective analysis of measles in Sao Paulo"
]
},
{
View PyStan.ipynb
1 2 3 4 5 6 7 8 9 10
{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
Something went wrong with that request. Please try again.