About the fisher matrix of Normal Distribution
Yingrui-Z opened this issue · comments
Question 1:
The first partial derivative of the log of normal distribution (l=-log(N~(μ,σ^2)))
with respect to the parameters μ and σ is
- ∂l/∂μ = (μ - x)/σ^2
- ∂l/∂σ = (σ^2 - (μ - x)^2)/σ^3 = 1/σ * (1-(μ - x)^2)/σ^2)
In ngboost, the corresponding implementation is:
def d_score(self, Y):
D = np.zeros((len(Y), 2))
D[:, 0] = (self.loc - Y) / self.var
D[:, 1] = 1 - ((self.loc - Y) ** 2) / self.var
return D
This raises a question: why is D[:, 1]
set to 1 - ((self.loc - Y) ** 2) / self.var
instead of 1/sqrt(self.var)*(1 - ((self.loc - Y) ** 2) / self.var)
Question 2:
Besides, based on the information from Wikipedia on Normal Distribution, the Fisher Information matrix for a normal distribution is defined as follows:
- F[0,0] = 1\σ^2
- F[1,1] = 2\σ^2.
However, in the ngboost
implementation, specifically in the NormalLogScore
class within normal.py
, the code snippet is:
def metric(self):
FI = np.zeros((self.var.shape[0], 2, 2))
FI[:, 0, 0] = 1 / self.var
FI[:, 1, 1] = 2
return FI
This raises another question: why is FI[1, 1]
set to 2
instead of 2\self.var
as per the theoretical formula?
Could you please clarify this discrepancy? Thank you for your assistance.
Both your questions revolve around using \mu
, `\sigma^` parametrization for the distribution (for gradients and Fisher information).
In NGBoost the parametrization is \mu
, \log \sigma^2
. If you work out the math with this parametrization, you should see the expressions match the implementation in the code.
Thank you for your kind explanation!
I am delving into a probability density function (pdf) that is defined as follows:
p(x | a, b, c) = a * b * exp(-a * (x - c)) / (1 + exp(-a * (x - c))) ^ (b + 1)
Here, a
, b
, and c
represent the parameters. Calculating the first-order derivatives and the Fisher Information Matrix for these parameters has proven to be exceptionally complex.
In contexts involving Normal distributions, transformations such as new_σ = log(σ^2)
have significantly simplified calculations. However, given the complex multiplicative interactions between the parameters in this pdf, implementing similar transformations poses a challenge.
Could you offer any insights or suggestions on how to transform this pdf to simplify the formulation of the Fisher Information Matrix?
Both your questions revolve around using
\mu
,\sigma^
parametrization for the distribution (for gradients and Fisher information).In NGBoost the parametrization is
\mu
,\log \sigma^2
. If you work out the math with this parametrization, you should see the expressions match the implementation in the code.