- 'effects' (Canon
ExpSim LV). The
first general-use interchangeable-lens
DSLRs with live view for both
exposure simulated live
preview (
ExpSim LV) and framing...
-
microphone Live
preview with
ExpSim LV "exposure simulation" live
preview (full
exposure preview control utilizing ExpSim LV, a
first for
video in a DSLR)...
- with
shape parameter 1. If X ~
Exp(λ) and Xi ~
Exp(λi) then: k X ∼
Exp ( λ k ) {\displaystyle kX\
sim \operatorname {
Exp} \left({\frac {\lambda }{k}}\right)}...
- {\boldsymbol {X}}\
sim {\mathcal {N}}({\boldsymbol {\mu }},\,{\boldsymbol {\Sigma }})} is a
multivariate normal distribution, then Y i =
exp ( X i ) {\displaystyle...
- ( α σ ) 2 ) = α σ 2 π x 3
exp ( − ( α − ν x ) 2 2 σ 2 x ) {\displaystyle T_{\alpha }=\inf\{t>0\mid X_{t}=\alpha \}\
sim \operatorname {IG} \left({\frac...
- that E 0 ∼
Exp ( 6 ) {\displaystyle E_{0}\
sim {\text{
Exp}}(6)} , E 1 ∼
Exp ( 12 ) {\displaystyle E_{1}\
sim {\text{
Exp}}(12)} and E 2 ∼
Exp ( 18 ) {\displaystyle...
- }}\right)\
sim {\textrm {GEV}}(\mu ,\,\sigma ,\,0)\ } If X ∼ GEV ( μ , σ , 0 ) {\displaystyle \ X\
sim {\textrm {GEV}}(\mu ,\,\sigma ,\,0)\ } then σ
exp ...
- F − 1 {
exp [ − j ω μ X ]
exp [ − σ X 2 ω 2 2 ]
exp [ − j ω μ Y ]
exp [ − σ Y 2 ω 2 2 ] } = F − 1 {
exp [ − j ω ( μ X + μ Y ) ]
exp [ − (...
- ^{2};\mu _{0},n_{0})&\
sim {\mathcal {N}}(\mu _{0},\sigma ^{2}/n_{0})={\frac {1}{\sqrt {2\pi {\frac {\sigma ^{2}}{n_{0}}}}}}\
exp \left(-{\frac {n_{0}}{2\sigma...
- , c ) {\displaystyle X\
sim {\textrm {Inv-Gamma}}(1,c)} then 1 X ∼
Exp ( c ) {\displaystyle {\tfrac {1}{X}}\
sim {\textrm {
Exp}}(c)\,} (Exponential distribution)...
