Statistics Basic Notes
🥥 Table of Content
- I. Probability
- II. Calculus
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I. Probability
- 01 - Conditional Probability & Joint Probaility
- 02 - Generative Model
- Naive Bayes
- Hidden Markov Model(HMM)
- 03 - Discriminative Model
- Logistic Regression
- Maximum Entropy Markov Model(MEMM)
- Conditional Random Fields(CRF)
01 - Conditional Probability & Joint Probaility
\(P(x, y) = P(x|y)P(y)\)
In terms of probability:
II. Calculus
Hessian Matrix | Baidu Baike
import sympy as sp
# Define the variables
x, y = sp.symbols('x y')
# Define the function
f = x**2 + 3*y**2
# Compute the Hessian
hessian = sp.hessian(f, [x, y]).tolist()
print(hessian)