Overview - Maple Help

Simulation Commands

 The Statistics package provides optimized algorithms for simulating from all supported distributions as well as tools for creating custom random number generators, parametric and non-parametric bootstrap.

 compute bootstrap statistics sample a kernel density estimate generate random sample

Examples

 > $\mathrm{with}\left(\mathrm{Statistics}\right):$

Generate random sample drawn from the non-central beta distribution.

 > $X≔\mathrm{RandomVariable}\left(\mathrm{NonCentralBeta}\left(3,10,2\right)\right):$
 > $A≔\mathrm{Sample}\left(X,1000\right)$
 ${A}{≔}\left[{0.298749038049262}{,}{0.160496089457820}{,}{0.324127131506297}{,}{0.290206966234071}{,}{0.537220813028702}{,}{0.225806784723962}{,}{0.323656644296660}{,}{0.224075578813581}{,}{0.139203619269065}{,}{0.144713788483976}{,}{0.162170989508424}{,}{0.163919547946135}{,}{0.404909255935277}{,}{0.412044080355721}{,}{0.348094710719217}{,}{0.331157628015845}{,}{0.295105552445666}{,}{0.193177607186590}{,}{0.400373007871060}{,}{0.130898465329786}{,}{0.195185443150392}{,}{0.376386657719254}{,}{0.0871782064096334}{,}{0.251491595894991}{,}{0.620308537159290}{,}{0.277296883006483}{,}{0.437779720079008}{,}{0.152921471824328}{,}{0.235265960512657}{,}{0.136378679376410}{,}{0.252714543040837}{,}{0.429187139161412}{,}{0.374226424815941}{,}{0.302801012538073}{,}{0.154684837824354}{,}{0.356035545600185}{,}{0.364686121969868}{,}{0.222026138646806}{,}{0.416210165850863}{,}{0.190871901430894}{,}{0.303495622736175}{,}{0.388708191892459}{,}{0.0738816418814309}{,}{0.202500852820107}{,}{0.104659380876615}{,}{0.354257658106483}{,}{0.208477128105665}{,}{0.382524697999167}{,}{0.176268646694049}{,}{0.120614541942356}{,}{0.183628521334067}{,}{0.105348882457644}{,}{0.322876552017316}{,}{0.199044448794066}{,}{0.351886259492336}{,}{0.541017418216111}{,}{0.400262939648824}{,}{0.289634276207489}{,}{0.136612348557671}{,}{0.429606223713469}{,}{0.311378762025564}{,}{0.237936821359705}{,}{0.172682713203868}{,}{0.587851925462311}{,}{0.222871011457330}{,}{0.107369139160512}{,}{0.389056233892129}{,}{0.345966852569480}{,}{0.284198344230638}{,}{0.149470287155729}{,}{0.328740299838745}{,}{0.265539779603986}{,}{0.156255385188462}{,}{0.329479875992677}{,}{0.257023076440196}{,}{0.240540840738284}{,}{0.182587825634373}{,}{0.426257901690903}{,}{0.104729408184404}{,}{0.232881839115843}{,}{0.305170632862252}{,}{0.145058470328692}{,}{0.261365502435354}{,}{0.0936526620439586}{,}{0.225512556744244}{,}{0.270964742331662}{,}{0.350022007063060}{,}{0.261919227464596}{,}{0.230771541043555}{,}{0.337509810728467}{,}{0.156134695828703}{,}{0.410821484550497}{,}{0.0689142635904654}{,}{0.142203876621286}{,}{0.286281908777234}{,}{0.251579621833158}{,}{0.193769396232071}{,}{0.138728250394330}{,}{0.100386830245882}{,}{0.350686151055232}{,}{\dots }{,}{\text{⋯ 900 row vector entries not shown}}\right]$ (1)

Use the bootstrap to estimate the mean and the standard error of the mean.

 > $\mathrm{Bootstrap}\left(\mathrm{Mean},X,\mathrm{replications}=1000,\mathrm{output}=\left['\mathrm{value}','\mathrm{standarderror}'\right]\right)$
 $\left[{0.282229356166131}{,}{0.00399644288773034952}\right]$ (2)
 > $\mathrm{Bootstrap}\left(\mathrm{Mean},A,\mathrm{replications}=1000,\mathrm{output}=\left['\mathrm{value}','\mathrm{standarderror}'\right]\right)$
 $\left[{0.275083017014237}{,}{0.00384426186874453797}\right]$ (3)

Compare this with analytic results.

 > $\mathrm{Mean}\left(X\right)$
 ${-}{1762148409}{+}{4790016000}{}{{ⅇ}}^{{-1}}$ (4)
 > $\mathrm{evalf}\left[30\right]\left(\mathrm{Mean}\left(X\right)\right)$
 ${0.28226746351970438745}$ (5)
 > $\mathrm{Mean}\left(X,\mathrm{numeric}\right)$
 ${0.2822674635}$ (6)

Random sample involving two independent random variables.

 > $Y≔\mathrm{RandomVariable}\left(\mathrm{Cauchy}\left(0,1\right)\right)$
 ${Y}{≔}{\mathrm{_R0}}$ (7)
 > $Z≔\mathrm{RandomVariable}\left(\mathrm{Cauchy}\left(1,2\right)\right)$
 ${Z}{≔}{\mathrm{_R1}}$ (8)
 > $B≔\mathrm{Sample}\left({Y}^{2}+{Z}^{2},{10}^{5}\right)$
 ${B}{≔}\left[{8.23027702966309}{,}{2.45150974710626}{,}{45.2464988258304}{,}{1.23619182280984}{,}{12.3761324894683}{,}{3.21707384976341}{,}{273.615015693467}{,}{5.96219698844479}{,}{0.860431105271889}{,}{0.305122978658424}{,}{7.52920327212682}{,}{2.07256467494591}{,}{10.8070815024917}{,}{0.394430182726394}{,}{19.3647570921962}{,}{0.461755743457343}{,}{6.24947599300303}{,}{35.9864514138204}{,}{2.67450203582295}{,}{1.11744947782124}{,}{195.557563933831}{,}{0.0208535989823628}{,}{3.30404958487121}{,}{14.7641389037312}{,}{68.2019138584949}{,}{7.13908974267329}{,}{21.4868999654586}{,}{112.153409360027}{,}{20.0821006324119}{,}{1.00955124266587}{,}{22.6997643045565}{,}{301.588167115830}{,}{9.08732884165008}{,}{1428.69647130412}{,}{106.801064245015}{,}{34.6040182577412}{,}{42.3065232455375}{,}{1.46416572945411}{,}{27.6371248343772}{,}{6.19919326889794}{,}{631.357700920488}{,}{9.66358067072234}{,}{2.72451268812064}{,}{15.8651635674137}{,}{25.9561529493941}{,}{85658.4898234459}{,}{3.59006552648215}{,}{721.450092993733}{,}{7.44843615212807}{,}{32.8907665534797}{,}{69.9386364891994}{,}{19.8179826337963}{,}{14.6471711656540}{,}{140.991079663595}{,}{4.44992272594081}{,}{118.929896846639}{,}{9.23111260596482}{,}{79.9062905993280}{,}{49.8924022414821}{,}{0.171457515992941}{,}{66.5417981810520}{,}{388.930721959224}{,}{2.76608294543268}{,}{2758.95321626323}{,}{8.07598807653631}{,}{8.84264595290888}{,}{107.144002184456}{,}{10.3110572416457}{,}{7.41556584460897}{,}{53.1177020373101}{,}{0.00322764364053573}{,}{219.543615893359}{,}{77.1015624199154}{,}{2.71494708348748}{,}{47.4094889091689}{,}{17559.9273444821}{,}{581.720994705186}{,}{4.28944838265206}{,}{13.3850469840742}{,}{6.87480101410390}{,}{49.3806015960269}{,}{4.62774610319884}{,}{128.008178020047}{,}{1.01250372667289}{,}{0.469692691233665}{,}{1.90436257347381}{,}{243.474478532672}{,}{1859.63495612918}{,}{44.1125836419248}{,}{28.6650544559048}{,}{25.8410892746042}{,}{176.549384396297}{,}{68.1964282117659}{,}{3.03672275380763}{,}{0.356483987731506}{,}{27.7072541434004}{,}{6.33505376468536}{,}{45.4290425286688}{,}{1.89815660635712}{,}{239.483036353238}{,}{\dots }{,}{\text{⋯ 99900 row vector entries not shown}}\right]$ (9)