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Min of loss: -0.47752660512924194
a1 = array([2.7837868], dtype=float32) b1 = array([-0.619422], dtype=float32) thetas = array([-2.06867695e-01, -1.03380226e-01, 1.07243657e-04, 1.04016328e+00, 1.04392898e+00, 1.05085635e+00, 1.64020348e+00, 2.62963533e+00, 2.96484590e+00, 3.20514488e+00, 3.57585144e+00, 3.94655800e+00], dtype=float32)
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loss: -0.8019787669181824 val_loss: -0.852887749671936
BernsteinFlow: invert_chain_of_bernstein_bijector_of_scale1_of_shift1: chain_of_bernstein_bijector_of_scale1_of_shift1: bernstein_bijector: [-3.0002496 -2.1074798 -1.21471 -0.89815176 -0.13984221 -0.13848484 -0.13847476 -0.13846476 -0.13845477 -0.13844477 -0.13843477 -0.13842477 -0.13841477 -0.1384047 -0.13839447 -0.13834201 12.404278 12.606189 12.8081 ] scale1: 0.4117906391620636 shift1: 0.6699955463409424
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Old Faithful
Learning Curve
Metrics
Min of loss: -0.47752660512924194
Parameter Vector
Flow
Results
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Bimodal Model
Learning Curve
Learning Curve
Metrics
Results
Parameter Vector for x = 1
BernsteinFlow:
invert_chain_of_bernstein_bijector_of_scale1_of_shift1:
chain_of_bernstein_bijector_of_scale1_of_shift1:
bernstein_bijector: [-3.0002496 -2.1074798 -1.21471 -0.89815176 -0.13984221 -0.13848484
-0.13847476 -0.13846476 -0.13845477 -0.13844477 -0.13843477 -0.13842477
-0.13841477 -0.1384047 -0.13839447 -0.13834201 12.404278 12.606189
12.8081 ]
scale1: 0.4117906391620636
shift1: 0.6699955463409424
Flow
Bijector