feat: add batch support for mixture evaluations

[plidan123]

• Extended moment, pdf, cdf, and logpdf methods to support both single values and lists of inputs for batch computation.
• Improved usability and performance for vectorized mixture evaluations.
• Fixed type annotation issues to resolve mypy errors and ensure compliance with abstract base class method signatures.

This enhancement improves usability and performance for batch evaluations.

[Desiment]

Instead of having in each class two methods:

class NMMixture:
    compute_pdf(self, ...):
        ... 
    compute_pdfs(self, ...):
        ...

It is much preferable to make "public" and "private" variants, where "public" variant defined in base class and handles inputs, and "private" variant defined in child class:

class AbstractMixture:
  @abstractmethod
  # "private" variant that containts computation logic for one input 
  def _compute_pdf(x: float, ...) -> float:
      ...
  # "public" variant that handles input type
  def compute_pdf(x: List[float] | float | NDArray[np.float64], ...) -> List[float] | float | NDArray[np.float64]
      # handle input type
      if isinstance(x, np.NDArray[float]):
          return np.array([self._compute_pdf(xp) for xp in x])
      if isinstance(x, list):
          return [self._compute_pdf(xp) for xp in x]
      if isinstance(x, float):
          return self._compute_pdf(x)

Closing, because waiting for #40 to be merged