analistica/slides/sections/3.md

Moyal distribution

Moyal PDF

:::: {.columns align=center}

::: {.column width=50%} Standard form:

  M(z) = \frac{1}{\sqrt{2 \pi}} \exp
         \left[ - \frac{1}{2} \left( z + e^{-z} \right) \right]

. . .

More generally:

• location parameter \mu
• scale parameter \sigma

  M_{\mu \sigma}(x) = \frac{1}{\sqrt{2 \pi} \sigma} \exp
        \left[ - \frac{1}{2} \left(
          \frac{x - \mu}{\sigma}
          + e^{-\frac{x - \mu}{\sigma}} \right) \right]

:::

::: {.column width=50%} :::

::::

Moyal CDF

The CDF F_M(x) can be derived by direct integration:

  F_M(x) = \int\limits_{- \infty}^x dy \, M(y)
  = \frac{1}{\sqrt{2 \pi}} \int\limits_{- \infty}^x dy \, e^{- \frac{y}{2}}
  e^{- \frac{1}{2} e^{-y}}

. . .

after a bit of math, one finally gets:

  F_M(x) = 1 - \text{erf} \left( \frac{e^{- \frac{x}{2}}}{\sqrt{2}} \right)

Moyal QDF

The quantile (CDF\textsuperscript{-1}) is found solving for x:

  y = 1 - \text{erf} \left( \frac{e^{- \frac{x}{2}}}{\sqrt{2}} \right)

hence:

  Q_M(y) = -2 \ln \left[ \sqrt{2} \, \text{erf}^{-1} (1 - y) \right]

Moyal median

Defined by F(m) = \frac{1}{2} or m = Q \left( \frac{1}{2} \right):

\begin{align*} M(z) &\thus m_M\ex = -2 \ln \left[ \sqrt{2} , \text{erf}^{-1} \left( \frac{1}{2} \right) \right] \ M_{\mu \sigma}(x) &\thus m_M\ex = \mu -2 \sigma \ln \left[ \sqrt{2} , \text{erf}^{-1} \left( \frac{1}{2} \right) \right] \end{align*}

\setbeamercovered{}

\begin{center} \begin{tikzpicture}[overlay] \pause \node [opacity=0.5, xscale=0.55, yscale=0.4 ] at (1.85,1.1) {\includegraphics{images/high.png}}; \end{tikzpicture} \end{center}

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Moyal mode

Peak of the PDF:

  \partial_x M(x) = \partial_x \left( \frac{1}{\sqrt{2 \pi}} e^{-\frac{1}{2}
                    \left( x + e^{-x} \right)} \right)
                  = \frac{1}{\sqrt{2 \pi}} e^{-\frac{1}{2}
                    \left( x + e^{-x} \right)} \left( -\frac{1}{2} \right)
                    \left( 1 - e^{-x} \right)

\begin{align*} \partial_x M(z) = 0 &\thus \mu_M\ex = 0 \ \partial_x M_{\mu \sigma}(x) = 0 &\thus \mu_M\ex = \mu \ \end{align*}

\setbeamercovered{}

\begin{center} \begin{tikzpicture}[overlay] \pause \node [opacity=0.5, xscale=0.18, yscale=0.25 ] at (2.4,1.8) {\includegraphics{images/high.png}}; \end{tikzpicture} \end{center}

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Moyal FWHM

We need to compute the maximum value:

  M(\mu_M\ex) = \frac{1}{\sqrt{2 \pi e}} \thus M(x_{\pm}) = \frac{1}{2 \, \sqrt{2 \pi e}}

. . .

which leads to:

  x_{\pm} + e^{-x_{\pm}} = 1 + 2 \ln(2) \thus
  \begin{cases}
    x_+ = 1 + 2 \ln(2) + W_0 \left( - \frac{1}{4 e} \right)    \\
    x_- = 1 + 2 \ln(2) + W_{-1} \left( - \frac{1}{4 e} \right)
  \end{cases}

Moyal FWHM

  x_+ - x_- = 3.590806098... = a

\begin{align*} M(z) &\thus w_M^{\text{exp}} = a \ M_{\mu \sigma}(x) &\thus w_M^{\text{exp}} = \sigma \cdot a \ \end{align*}

\setbeamercovered{}

\begin{center} \begin{tikzpicture}[overlay] \pause \node [opacity=0.5, xscale=0.2, yscale=0.25 ] at (1.9,1.9) {\includegraphics{images/high.png}}; \end{tikzpicture} \end{center}

\setbeamercovered{transparent}