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@jonathansick
Last active August 29, 2015 14:06
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Probabilistic Graphical Model of Multi-Pixel SEDs drawn in Daft
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{
"metadata": {
"name": "",
"signature": "sha256:54fe8f190391d2521af5e0a1d065c04c453b7e3ca684d4bc7f12e9ece6f76d92"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Multi-Pixel SED Modelling with Hierarchical Gibbs Sampling"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"import matplotlib as mpl\n",
"mpl.rc('font', size=22)\n",
"from daft import PGM, Node, Plate"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 55
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pgm = PGM([4, 4], origin=[0, 0], observed_style='inner', grid_unit=4, node_unit=3)\n",
"pgm.add_node(Node(\"theta\", r\"$\\mathbf{\\theta}_n$\", 3, 3))\n",
"pgm.add_node(Node(\"f\", r\"$\\mathbf{f}_n$\", 2, 3))\n",
"pgm.add_node(Node(\"sigma\", r\"$\\mathbf{\\sigma}_n$\", 3, 2, observed=True))\n",
"pgm.add_node(Node(\"F\", r\"$\\mathbf{F}_n$\", 2, 2, observed=True))\n",
"pgm.add_node(Node(\"B\", r\"$\\mathbf{B}$\", 1, 2))\n",
"pgm.add_node(Node(\"phi\", r\"$\\mathbf{\\phi}$\", 2, 1))\n",
"pgm.add_plate(Plate((1.5, 1.5, 2, 2.3), label=r\"Pixel $n$\", label_offset=[15, 235], shift=0, rect_params={}))\n",
"pgm.add_edge(\"theta\", \"f\")\n",
"pgm.add_edge(\"f\", \"F\")\n",
"pgm.add_edge(\"sigma\", \"F\")\n",
"pgm.add_edge(\"B\", \"F\")\n",
"pgm.add_edge(\"phi\", \"F\")\n",
"pgm.render()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 58,
"text": [
"<matplotlib.axes.Axes at 0x10b79b0d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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r+EzgMhRO0bq5zNK0EJgduhBVjeOAzcwHbmlyfAG6eTe7cWcpxDKVhWS/HCfuc8ARKCnD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\nBSmo8Sh/7moJz5O0Ap8AfD3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"text": [
"<matplotlib.figure.Figure at 0x10b799810>"
]
}
],
"prompt_number": 58
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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