title: Brownian meander
text: # Brownian meander
  
  In the mathematical theory of probability, Brownian meander $W^{+}=\{W_{t}^{+},t\in [0,1]\}$ is a continuous non-homogeneous Markov process defined as follows:
  
  Let $W=\{W_{t},t\geq 0\}$ be a standard one-dimensional Brownian motion, and $\tau :=\sup\{t\in [0,1]:W_{t}=0\}$, i.e. the last time before t = 1 when $W$ visits $\{0\}$. Then the Brownian meander is defined by the following:
  
      $W_{t}^{+}:={\frac {1}{\sqrt {1-\tau }}}|W_{\tau +t(1-\tau )}|,\quad t\in [0,1].$
  
  In words, let $\tau$ be the last time before 1 that a standard Brownian motion visits $\{0\}$. ($\tau <1$ almost surely.) We snip off and discard the trajectory of Brownian motion before $\tau$, and scale the remaining part so that it spans a time interval of length 1. The scaling factor for the spatial axis must be square root of the scaling factor for the time axis. The process resulting from this snip-and-scale procedure is a Brownian meander. As the name suggests, it is a piece of Brownian motion that spends all its time away from its starting point $\{0\}$.
  
  The transition density $p(s,x,t,y)\,dy:=P(W_{t}^{+}\in dy\mid W_{s}^{+}=x)$ of Brownian meander is described as follows:
  
  For $0<s<t\leq 1$ and $x,y>0$, and writing
  
      $\varphi _{t}(x):={\frac {\exp\{-x^{2}/(2t)\}}{\sqrt {2\pi t}}}\quad {\text{and}}\quad \Phi _{t}(x,y):=\int _{x}^{y}\varphi _{t}(w)\,dw,$
  
  we have
  
      ${\begin{aligned}p(s,x,t,y)\,dy:=&P(W_{t}^{+}\in dy\mid W_{s}^{+}=x)\\=&{\bigl\varphi _{t-s}(y-x)-\varphi _{t-s}(y+x){\bigl )}{\frac {\Phi _{1-t}(0,y)}{\Phi _{1-s}(0,x)}}\,dy\end{aligned}}$
  
  and
  
      $p(0,0,t,y)\,dy:=P(W_{t}^{+}\in dy)=2{\sqrt {2\pi }}{\frac {y}{t}}\varphi _{t}(y)\Phi _{1-t}(0,y)\,dy.$
  
  In particular,
  
      $P(W_{1}^{+}\in dy)=y\exp\{-y^{2}/2\}\,dy,\quad y>0,$
  
  i.e. $W_{1}^{+}$ has the Rayleigh distribution with parameter 1, the same distribution as ${\sqrt {2\mathbf {e} }}$, where $\mathbf {e}$ is an exponential random variable with parameter 1.
  
  