Junpeng Lao junpenglao

## MvNormal_pymc3.ipynb

      
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                / MvNormal_pymc3.ipynb
            
            
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              March 29, 2021 00:49
            
              
                Multivariant Gaussian distribution estimation in PyMC3 using different prior
              
          
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## PyMC3_tips_and_heuristic.ipynb

      
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## Nonlinear_funcs.ipynb

      
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                / Nonlinear_funcs.ipynb
            
            
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              February 7, 2018 23:05
            
          
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## theano-jax-test-drive.ipynb

      
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              November 10, 2020 07:46
            
              
                theano-jax test drive.ipynb
              
          
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## mixture_tfp.ipynb

      
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              October 19, 2020 13:10
            
              
                mixture_tfp.ipynb
              
          
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## tfp_nuts_demo.ipynb

      
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              January 13, 2020 15:13
            
              
                TFP_NUTS_demo.ipynb
              
          
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## [WIP] Bayesian GMM.ipynb

      
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              October 18, 2019 11:11
            
              
                Documents/Github/Human_Learning/Miscellaneous/[WIP] Bayesian GMM.ipynb
              
          
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## Dirichlet_random_area_plot.py
import numpy as np
import scipy.stats as st
import matplotlib.pyplot as plt
plt.style.use('ggplot')

pmat = np.vstack([np.linspace(1, 20, 20),
                  np.linspace(2, 10, 20),
                  np.linspace(3, 5, 20)]).T

p = np.asarray([st.dirichlet.rvs(p_, 200) for p_ in pmat])

## OOS_Predict_with_Missing.ipynb

      
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                / OOS_Predict_with_Missing.ipynb
            
            
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              May 9, 2018 22:58
            
              
                OOS Prediction for regression model with missing data
              
          
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## magic_box_theano.py
import numpy
import theano
import theano.tensor as tt
from theano.gradient import disconnected_grad as stop_grad

x = tt.dscalar('x')
y = x ** 2
gy = tt.grad(y, x)

f = theano.function([x], gy)
	import numpy as np
	import scipy.stats as st
	import matplotlib.pyplot as plt
	plt.style.use('ggplot')

	pmat = np.vstack([np.linspace(1, 20, 20),
	np.linspace(2, 10, 20),
	np.linspace(3, 5, 20)]).T

	p = np.asarray([st.dirichlet.rvs(p_, 200) for p_ in pmat])
	import numpy
	import theano
	import theano.tensor as tt
	from theano.gradient import disconnected_grad as stop_grad

	x = tt.dscalar('x')
	y = x ** 2
	gy = tt.grad(y, x)

	f = theano.function([x], gy)