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1 | 1 | # Release Notes
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2 |
| -## PyMC3 3.0 (September xx, 2016) |
| 2 | +## PyMC3 3.0 (January 7, 2017) |
3 | 3 |
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4 | 4 | We are proud and excited to release the first stable version of PyMC3, the product of more than [5 years](https://github.com/pymc-devs/pymc3/commit/85c7e06b6771c0d99cbc09cb68885cda8f7785cb) of ongoing development and contributions from over 80 individuals. PyMC3 is a Python module for Bayesian modeling which focuses on modern Bayesian computational methods, primarily gradient-based (Hamiltonian) MCMC sampling and variational inference. Models are specified in Python, which allows for great flexibility. The main technological difference in PyMC3 relative to previous versions is the reliance on Theano for the computational backend, rather than on Fortran extensions.
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5 | 5 |
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57 | 57 | Andres Asensio Ramos
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58 | 58 |
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59 | 59 | Anjum48
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60 |
| -AustinRochford <[email protected]> |
| 60 | +Austin Rochford <[email protected]> |
61 | 61 | Benjamin Edwards <[email protected]>
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62 | 62 |
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63 | 63 | Brian Naughton <[email protected]>
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64 | 64 | Byron Smith
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65 | 65 |
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66 | 66 | Chris Fonnesbeck < [email protected]>
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67 |
| -Colin |
| 67 | +Colin Carroll |
68 | 68 |
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69 | 69 |
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70 | 70 |
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89 | 89 | Lin Xiao
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90 | 90 |
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91 | 91 | Matthew Emmett <[email protected]>
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92 |
| -Maxim |
| 92 | +Maxim Kochurov |
93 | 93 | Michael Gallaspy < [email protected]>
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94 | 94 |
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95 | 95 | Osvaldo Martin <[email protected]>
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104 | 104 | Thomas Kluyver <[email protected]>
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105 | 105 |
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106 | 106 |
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107 |
| -Volodymyr |
108 | 107 | Volodymyr Kazantsev
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109 | 108 |
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110 | 109 |
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115 | 114 | ingmarschuster < [email protected]>
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116 | 115 |
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117 | 116 | jason <JasonTam22@gmailcom>
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118 |
| - |
| 117 | + |
119 | 118 |
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120 | 119 |
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121 | 120 |
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126 | 125 |
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127 | 126 |
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128 | 127 | stonebig <stonebig>
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129 |
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130 |
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| 128 | + |
131 | 129 |
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132 | 130 |
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133 | 131 |
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