学《高级统计学》的时候，在介绍MCMC及EM的时候，老师介绍过这个软件，
非常好！就是我自己编不出程序来。我是统计的爱好老师与初学者。
另外，问一下谢老师，openbugs在那儿有下载的呢？
非常感谢！
非常好！就是我自己编不出程序来。我是统计的爱好老师与初学者。
另外，问一下谢老师，openbugs在那儿有下载的呢？
非常感谢！
WinBUGS在统计分析中的应用（第一部分）
[未知用户] 都这年头了，真的需要我把网址打出来么？……
[未知用户] 去google一下就万事大吉了:)
1 个月 后
请问谢老师，我的问题也是
它说edicational version cannot do this model，是什么原因呢？谢谢了
它说edicational version cannot do this model，是什么原因呢？谢谢了
[未知用户] 谢老师，上面的问题急待回答，谢谢。
谢谢，不好意思。不小心没看到前面有一个贴的回复。
1 个月 后
[未知用户] 请问，上面这位仁兄，你的问题解决了吗？我现在也遇到与你同样的问题。到底问题出在哪里呢？
2 个月 后
[未知用户] 请教谢老师，我用winbugs做SVCJ（带跳的随机波动率）模型，MODEL检验和上传数据都行，就是到了load inits那里总是提示有uninitialized variables，可我该设的参数的初始值差不多都设了，仔细检查了不知道还有什么变量没设。如果采用软件自动赋值初始化（gen inits）的话，只要数据稍多一点，比如500条记录，软件就半天没反应了，而如果只是几十条数据的话还行。不知道是不是编的程序的问题？另还有什么参数没设初值呢？
model;
{
mu ~ dnorm(1,0.4)
for( i in 2 : n ) {
v ~ dnorm(vmean,ivd)I(0,50)
}
kt ~ dnorm( 0.0, 1.0)
k ~ dnorm( 0.0, 1.0)
# theta <- kt / k
for( i in 2 : n ) {
ksy ~ dnorm(ksymean,tauy)
}
for( i in 2 : n ) {
ksv ~ dexp(muv)
}
rhoj ~ dnorm( 0.0,4)
muv ~ dgamma(1,0.1)
for( i in 2 : n ) {
j ~ dbern(lambda)
}
lambda ~ dbeta(1,20)
#ev ~ dnorm( 0.0, 1.0)
#ey ~ dnorm( 0.0, 1.0)
#e ~ dnorm( 0.0,1.0E-6)
sigy2 <- 1 / tauy
tauy ~ dgamma( 1,0.1)
muy ~ dnorm( 0.0,0.1)
for( i in 2 : n ) {
#rmean <- mu + ksymean * j
rmean[i] <- mu + ksy[i] * j[i]
}
tauv ~ dgamma( 1, 0.1)
sigv2 <- 1 / tauv
for( i in 2 : n ) {
#vmean[i] <- v[i - 1] +( kt-k* v[i - 1]) + 1/muv * j[i] + rho * (y[i] - y[i - 1] - mu - ksymean[i] * j[i])
vmean[i] <- v[i - 1] +( kt-k* v[i - 1]) +ksv[i] * j[i] +sqrt(sigv2)* rho * (y[i] - y[i - 1] - mu - ksy[i] * j[i])
}
for( i in 2 : n ) {
#ivd[i] <- 1 / (rho*rho*sigy2*j[i]+(1-rho*rho)*sigv2*j[i]+v[i - 1]+1/pow(muv,2)*j[i])
ivd[i] <- tauv/((1-rho*rho)*v[i-1])
}
for( i in 2 : n ) {
#ird[i] <- 1 / (sigy2*j[i]+v[i - 1])
ird[i] <- 1/v[i-1]
}
for( i in 2 : n ) {
r[i] <- y[i]-y[i-1]
r[i] ~ dnorm(rmean[i],ird[i])
}
rho ~ dunif(-1,1)
for( i in 2 : n ) {
ksymean[i] <- muy + rhoj * ksv[i]
}
v[1] <- 1
ksv[1] <- 0.0
ksy[1] <- 0.0
j[1]<-1
}
list(y=c(3.274324143,3.287163251,3.284207325,3.273737212,3.279863062),
n=5)
list(mu=1,k=1,kt=1,tauv=1,rho=1,lambda=1,muy=1,tauy=1,muv=1,rhoj=1)
model;
{
mu ~ dnorm(1,0.4)
for( i in 2 : n ) {
v ~ dnorm(vmean,ivd)I(0,50)
}
kt ~ dnorm( 0.0, 1.0)
k ~ dnorm( 0.0, 1.0)
# theta <- kt / k
for( i in 2 : n ) {
ksy ~ dnorm(ksymean,tauy)
}
for( i in 2 : n ) {
ksv ~ dexp(muv)
}
rhoj ~ dnorm( 0.0,4)
muv ~ dgamma(1,0.1)
for( i in 2 : n ) {
j ~ dbern(lambda)
}
lambda ~ dbeta(1,20)
#ev ~ dnorm( 0.0, 1.0)
#ey ~ dnorm( 0.0, 1.0)
#e ~ dnorm( 0.0,1.0E-6)
sigy2 <- 1 / tauy
tauy ~ dgamma( 1,0.1)
muy ~ dnorm( 0.0,0.1)
for( i in 2 : n ) {
#rmean <- mu + ksymean * j
rmean[i] <- mu + ksy[i] * j[i]
}
tauv ~ dgamma( 1, 0.1)
sigv2 <- 1 / tauv
for( i in 2 : n ) {
#vmean[i] <- v[i - 1] +( kt-k* v[i - 1]) + 1/muv * j[i] + rho * (y[i] - y[i - 1] - mu - ksymean[i] * j[i])
vmean[i] <- v[i - 1] +( kt-k* v[i - 1]) +ksv[i] * j[i] +sqrt(sigv2)* rho * (y[i] - y[i - 1] - mu - ksy[i] * j[i])
}
for( i in 2 : n ) {
#ivd[i] <- 1 / (rho*rho*sigy2*j[i]+(1-rho*rho)*sigv2*j[i]+v[i - 1]+1/pow(muv,2)*j[i])
ivd[i] <- tauv/((1-rho*rho)*v[i-1])
}
for( i in 2 : n ) {
#ird[i] <- 1 / (sigy2*j[i]+v[i - 1])
ird[i] <- 1/v[i-1]
}
for( i in 2 : n ) {
r[i] <- y[i]-y[i-1]
r[i] ~ dnorm(rmean[i],ird[i])
}
rho ~ dunif(-1,1)
for( i in 2 : n ) {
ksymean[i] <- muy + rhoj * ksv[i]
}
v[1] <- 1
ksv[1] <- 0.0
ksy[1] <- 0.0
j[1]<-1
}
list(y=c(3.274324143,3.287163251,3.284207325,3.273737212,3.279863062),
n=5)
list(mu=1,k=1,kt=1,tauv=1,rho=1,lambda=1,muy=1,tauy=1,muv=1,rhoj=1)
21 天 后
[未知用户] 可以。Google Openbugs 的主页，上面又说明。我更喜欢linux下的版本。
另外，R里面有 R2bugs 的package，在数据不大的情况下，很方便。
另外，R里面有 R2bugs 的package，在数据不大的情况下，很方便。
1 个月 后
[未知用户] 你好，我是WINBUGS的初学者，现在想用来做SV-T模型的MCMC估计，但总提示数据载入不了，还有初始值设置也不明白，请帮忙指导一下，真的很急，谢谢！！！
model{
for(i in 1:n)
{y~dt(0,p,omega)
p<-exp(-theta)
}
theta[1]~dnorm(mu,itau2)
for(j in 2:n)
{theta[j]~dnorm(theta2[j],itau2)
theta2[j]<-mu+phi*(theta[j-1]-mu)}
phi<-2*phi1-1
tau<-sqrt(1/itau2)
mu~dnorm(0,0.01)
itau2~dgamma(2.5,0.025)
phi1~dbeta(20,1.5)
omega~dchisqr(8)
}
list(N=236， y=c(-2.863137825，0.236862576，0.708743605，-0.410171084，-0.197900711，-0.017711569，-1.099961877，
-0.131942478，-0.628070303，0.329370517，-0.425979456，0.003865601，-1.562805432，-1.029900337，0.30855106，
-1.76405028，0.536937996，0.88903611，-0.478923481，-2.231280334，0.95179952，-0.240613751，-0.444776657，
0.238243297，2.343495649，-1.345814335，0.409521478，0.938909523，-0.541400821，-1.120087252，-0.465308369，
0.22970175，-0.442665133，0.208833427，-0.866786092，0.294963792，1.018515417，-0.296166322，-0.055762223，
-0.336635628，-0.431943542，1.38828862，-0.107762343，-0.502041508，0.094863359，-0.266547417，-0.188964816，
-2.292527804，-0.355015921，-0.569077156，0.017514077，-0.25962396，0.583479024，0.068167933，0.014009904，
1.352042569，0.177236882，-0.564620898，0.349787556，-0.707430539，-0.129763575，1.671152083，0.776163597，
-0.021446623，0.632287045，-0.008614125，0.209323915，-0.289051354，1.681336675，-0.155862607，-0.077731799，
0.82786481，-1.041519493，0.261535927，-0.412195369，1.112557672，-0.100371081，-1.144249282，0.040589822，
-0.189283934，0.434968595，1.094428192，0.290705338，-0.0138222，0.283080016，-0.782503525，-0.085722785，
0.129237661，-0.863186702，0.146550995，0.106793381，0.866076389，-0.290592628，-0.298612901，0.585978478，
0.060169353，0.871721164，0.067681259，-0.016429211，-0.691862319，-0.099393894，0.397879547，-0.0049166，
-0.686676288，-0.782562372，-0.017342403，0.106779034，-0.020327393，0.4941572，-0.184977933，0.045901852，
0.883238737，1.461385829，1.173875952，0.238537867，0.716623196，0.32768553，1.063529252，0.24979205，
1.218106841，0.438037488，-0.11434406，0.212401723，1.731100734，-0.575671103，-0.861522307，0.323921825，
-1.171227643，1.687658722，-0.670770608，-0.347428641，0.656840307，0.654451244，0.023281919，-0.413060303，
-0.236891092，-0.290287567，-2.747778165，0.143692637，-1.71000562，-0.935081269，0.692751369，
-0.411491889，0.928809898，-0.926643719，1.04862006，0.412139218，-0.193032523，-0.009653293，-0.79853627，
-0.100936572，0.309291433，0.102463117，-0.050883159，0.694099117，-0.842781601，
-0.269146402，0.441508007，1.405110928，0.033024447，-0.328643359，-0.323136664，-0.09309922，
-0.158225088，1.260303693，-0.789521669，-0.337560523，-0.291325249，-1.066279143，
-0.618890031，0.248983641，0.034759251，0.882100279，0.48269568，-0.066941817，
-0.184608038，-0.023517406，-0.672468729，0.253540536，0.191561488，-0.087120801，-0.686079288，
-1.793905963，0.036004183，1.022793487，-1.434730455，0.589690201，-0.217817872，
-0.145532668，0.401124556，0.627431273，0.224008103，0.485978832，0.082761656，
-0.662787178，0.939936177，0.179767043，1.528823088，-0.165954634，0.393731707，-0.033624367，
-0.370122603，0.936833337，-1.404577085，-0.053542798，0.249369071，0.293947579，0.548939249，-0.006808959，
-0.11640434，-0.288842871，0.870856137，0.706003843，0.278474099，-0.229181126，-0.72134986，
-0.524715596，0.113296712，-0.787005295，0.586313224，
-0.47130353，0.155244387，0.154718569，0.025373314，0.50625468，-0.025369976，0.419284333，-0.019841149，
-0.297545391，-0.131656389，-0.25774229，0.435472774，0.420723963，0.210803862，0.551788416，-0.27312702))
list(mu=1, tau=2)[未知用户] 首先我想知道你了解这个模型吗？软件不是用来往里面乱塞东西的，尽管BUGS软件做得很简单（通常给定先验、给定数据，剩下的什么都不用管了），但你也得稍微明白一下这些代码的来龙去脉吧。比如你的模型中有个变量n，在数据中有吗？初始值中提供了一个变量tau，可是模型中它是参数吗？
2 个月 后
你好，我刚学WINBUGS软件，现在想用来做SV-N模型，但是在模型的编辑（compile）这一步时，软件总是显示array index is greater than array bound for y 不知为什么，麻烦请帮忙指导一下，真的很急，谢谢！！！
model
{
for(i in 1:N)
{y~dnorm(0,p)
p<-exp(-theta)
}
theta[1]~dnorm(mu,itau2)
for(j in 2:N)
{theta[j]~dnorm(theta2[j],itau2)
theta2[j]<-mu+phi*(theta[j-1]-mu)}
phi<-2*phi1-1
mu~dnorm(0,0.01)
itau2~dgamma(2.5,0.025)
phi1~dbeta(20,1.5)
}
list(N=1000,
y=c(-0.01332,0.215247,0.165196,0.268095,-0.08932,0.784046,0.194879,-0.55396,-0.06357,-0.75685,
-0.80325,-0.24164,-0.14004,-0.06396,-0.03859,0.03763,-0.41906,-0.2917,-0.01348,-0.01342,
-0.21579,0.189124,0.215324,0.778916,-0.60096,-0.97844,0.646815,-0.92657,0.341113,-0.08932,
-0.06394,-0.19079,-0.16536,0.393219,0.242184,0.064052,-0.08932,0.03867,-0.01245,-0.06368,
-0.06368,-0.01235,0.270668,0.764389,0.249,-0.42764,-0.01135,-0.45268,0.09219,0.300745,
-0.14142,-0.21945,0.275452,0.434102,-0.03683,-0.0368,0.015803,0.200342,0.016219,0.759039,
0.123898,-0.14267,-0.19551,-0.19539,-0.56526,-0.45794,0.252919,-0.74644,0.357049,-0.32588,
-0.0368,0.173698,0.306509,-0.27424,0.14849,0.361428,0.176777,0.204207,0.823436,0.072621,
-0.11633,-1.51045,-0.77916,-0.14219,0.414057,-0.61912,0.148742,0.255552,0.711109,-0.83659,
0.150263,-1.33421,-0.71908,0.093948,-0.97636,0.014628,-0.08932,-0.7889,-0.45015,-0.29492,
0.06484,0.348767,0.454509,0.248824,-1.28074,0.013718,0.323902,-0.03755,0.195911,0.248824,
-0.29755,0.197099,-0.08932,-0.11539,0.067213,0.38176,-0.06309,0.015692,0.252739,-0.03659,
-0.19475,0.042482,0.280657,0.149247,0.28292,-0.14258,0.123898,-0.06264,-0.03593,0.124525,
-0.14283,-0.16953,-0.08932,0.151481,0.286416,-0.08932,0.126021,-0.35843,-0.2236,0.018089,
-0.06245,-0.16991,-0.08932,-0.08932,0.233432,-0.11626,0.315482,0.154349,-0.17061,-0.00803,
-0.14352,-0.30583,-0.2244,0.045758,0.208499,-0.00794,-0.08932,-0.00788,0.346171,-0.55197,
-0.00783,-0.11649,0.209957,0.047011,0.40302,0.185253,0.351572,0.021206,-0.47564,-0.17191,
0.048362,-0.72111,-0.19879,0.266892,-0.28129,-0.11672,0.10259,0.130458,0.241257,0.408604,
0.188379,-0.58863,-0.03397,-0.14468,0.049125,0.104826,0.077391,-0.14492,-0.03372,0.35661,
-0.42396,0.049973,0.30174,-0.11731,0.247001,-0.03316,0.361129,0.136667,-0.1176,-0.14585,
-0.20228,0.08017,-0.54066,-0.20184,-0.03308,0.729776,-0.08932,0.280049,-0.57207,-0.96368,
-0.06124,-0.53775,0.359109,0.276515,0.193005,-0.65318,-0.65002,0.162603,0.388274,-0.03298,
-0.08932,0.136348,0.30683,0.393839,-0.37382,0.166687,0.195904,0.196719,-0.17522,0.082549,
-0.11799,-0.03198,-0.06064,-0.03193,-0.08932,0.313325,-0.49197,0.025555,0.198448,-0.29085,
-0.11808,0.227453,0.141692,-0.11823,-0.06042,-0.06041,-0.0604,-0.06039,-0.205,-0.69442,
-0.06059,0.025687,0.169932,0.112786,-0.0315,0.113312,-0.58072,-0.37726,0.314021,-0.37759,
0.343392,0.055333,-0.00243,0.317183,0.027126,0.290074,-0.08932,-0.00156,-0.14784,-0.08932,
-0.49796,-0.08932,-0.14756,0.582508,-0.84845,0.318723,0.320395,0.410459,0.028637,-0.20728,
0.590852,0.059156,-0.26747,0.237524,-0.2083,-0.14876,-0.23776,0.029408,-0.26736,0.118424,
-0.6522,-0.26642,-0.05983,-0.35446,-0.14815,0.14618,-0.05985,-0.53056,-0.11867,-0.26522,
0.115927,-0.47016,-0.23541,-0.38087,-0.14753,-0.69846,-0.08932,0.287326,-0.03125,-0.61076,
0.403076,0.844174,-0.26502,-0.26471,-0.78781,-0.08932,-0.35,-0.11824,-0.0604,-0.40701,-0.40601,
-0.06057,-0.4051,-0.2039,-0.3752,0.196556,-0.11795,-0.2609,-0.43159,-0.06084,-0.2885,
-0.62795,0.705459,0.110364,-0.71557,-0.06094,-0.1177,-0.06094,-0.08932,-0.74005,-0.62371,
-0.11737,-0.14539,-0.48093,-0.39594,0.049934,-0.42321,-0.20037,-0.36641,-0.06165,-0.22762,
0.27064,-0.25562,-0.06163,0.299244,0.300759,-0.14514,-0.5903,-0.56017,0.104289,-0.117,
-0.3105,0.353524,-0.00607,0.523323,-0.28466,0.357731,-0.00528,-0.42508,-0.14517,-0.86797,
0.021543,0.494743,0.217984,-0.31291,0.386403,-1.03852,0.691714,-0.64783,-0.39518,-0.61542,
-0.42017,-0.33674,-1.26308,-0.1707,-0.22481,-0.5486,-0.19708,-0.00851,-0.06237,-0.51966,
0.314067,-0.00845,-0.70769,-0.33025,-0.32967,0.204521,-1.10078,-0.1952,-0.32713,-0.32657,
0.438662,-0.27443,0.069324-0.14223,0.680627,-0.91215,-0.2478,0.572669,0.791271,0.071622,
-0.43771,-0.11607,1.311186,0.673001,-0.19858,-0.17119,-0.55197,-0.44167,-0.33253,0.316358,
-1.35499,0.151803,0.367729,-1.02804,0.28511,1.015364,0.45401,-0.84916,-1.45865,-1.01833,
0.26577,-1.961,-0.27197,0.171707,-1.85103,0.270853,1.783782,2.16811,-3.62531,-4.12899,
7.486337,-10.1731,-1.89421,0.125552,-2.59122,-1.0174,-0.02001,-0.57347,-0.73123,1.732474,
0.775472,0.145696,1.763023,-0.56756,-1.7923,-2.29282,0.324472,-1.12061,1.241889,1.823496,
-0.86352,0.944279,-0.84209,-0.27663,4.87394,-2.0368,-2.11779,-0.77208,0.216165,-3.44495,
-0.0438,1.401702,3.390926,-4.83265,-2.81318,0.378125,-1.24284,-1.4475,-0.5452,0.75897,
-0.3511,0.281736,1.120681,-1.08041,0.724853,0.420149,0.601489,1.148178,-0.08932,-0.08932,
-0.38321,0.772166,1.055641,0.072024,1.327823,0.992787,1.100318,1.308628,-0.62188,-0.90683,
1.06676,-2.10378,-0.82262,0.004995,1.264457,0.678805,0.00711,1.539893,0.64887,0.753937,
-0.98196,2.23317,-0.79426,-1.26155,-0.68265,0.826866,-2.78887,-0.83712,0.223592,-0.47431,
-0.85488,-0.42242,2.023095,-0.74221,-0.73797,-0.9002,-2.96907,0.095478,-0.6427,-1.95726,
-1.78997,-1.01689,1.193881,0.670628,0.789557,0.114598,-1.97659,0.804938,0.496265,-2.01332,
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2.659333,1.412502,-0.08932,0.108241,0.43943,1.851187,1.820695,0.393937,3.002983,0.555149,
0.559329,1.73488,-2.70575,-0.51881,0.411932,0.414457,-0.01715,0.999618,1.455394,0.505475,
1.716596,1.133,-0.39631,1.14431,0.689498,0.61684,-0.32526,0.14662,0.860052,0.548639,
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list(mu=0,itau1=0.02,phi1=0.975)
model
{
for(i in 1:N)
{y~dnorm(0,p)
p<-exp(-theta)
}
theta[1]~dnorm(mu,itau2)
for(j in 2:N)
{theta[j]~dnorm(theta2[j],itau2)
theta2[j]<-mu+phi*(theta[j-1]-mu)}
phi<-2*phi1-1
mu~dnorm(0,0.01)
itau2~dgamma(2.5,0.025)
phi1~dbeta(20,1.5)
}
list(N=1000,
y=c(-0.01332,0.215247,0.165196,0.268095,-0.08932,0.784046,0.194879,-0.55396,-0.06357,-0.75685,
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-0.16987,-0.00877,-0.57164,0.473607,0.395723,0.316687,0.809744,-0.00719,-0.25353,0.734406,
-0.00657,-0.08932,-1.64993,0.07381,0.565881,0.487523,1.074448,-1.17041,-1.89265,-0.97894,
1.861959,1.566345,0.412771,-0.00539,0.923345,0.676965,1.374329,0.084289,-1.29832,-0.60302,
0.338578,-0.08932,0.426586,0.515954,1.39607,1.329141,1.078181,-0.08932,-1.07809,1.715781,
0.092993,-0.08932,0.367926,0.831495,0.374286,-0.46038,0.281736,-0.18222,0.562753,-0.27606,
0.847892,-0.08932,-0.37141,1.043835,-0.18424,0.38619,-0.18461,-1.41384,-0.37088,-0.08932))
list(mu=0,itau1=0.02,phi1=0.975)
当我运行compile这一步是，软件界面上说educational version cannot do this model，是什么意思啊！有知道的麻烦告诉一声，有急用！谢谢
21 天 后
请问用openbugs时，前面模型都没有问题，但是在load inits这步，总显示unable to generate initial values for node [011483COH] of type Grapht.Mixing,是什么意思？难道初值设定有问题吗？谢谢
1 个月 后
请教大家一下，如果炫耀载入的数据非常多，如何载入那么多数据啊，有没有简便方法？
[未知用户] 更正一下，是需要载入的数据多
1 年 后
您好，winbugs软件有详细的说明吗
3 个月 后
等WinBUGS的update时，顺便把评论都看了一遍，觉得很多朋友对Bayes分析还是很有兴趣的，但椰丝儿们留错了地方，cos论坛的椰子板块会更靠谱。看到后来发现很多评论挂着一大长串代码的都在问一件事情：SV模型怎么做？这让我觉得很惊讶，不过想来也合理。
国内的金融方面最近几年一直很热，学生极多。很多学校会对金融的学生要求论文，而高质量的也是学生保研，各种评优的重要依据。最快的方法，就是用各种统计和计量方法来让自己的文章升级（记得有个神文写的是如何拿计量经济五大杀器力克CSSCI）。我接触的一些研究生，甚至是本科生，都在搞一些很高级的处理方法和模型（高级的意思是读一两本书都搞不清楚）。金融方面比较好用的，无非就是波动模型，因此在GARCH模型已经搞烂掉的现在，SV模型凭借它”拒人于千里之外“的难理解的特点越来越受到学生的青睐。这里的MCMC之所以被提如此之多，就是因为目前SV估计比较好的方法，就是它了。
有时候一想起这些就会有些郁闷，但觉得孩子们本身没错儿，靠自己的努力和智力，迅速学一些别人学不会的方法来争取自己的前程是很棒的。令我郁闷的是这种特别的规则而引发的浮躁心态以及对思考问题的态度。就拿SV来说，有个孩子学经济的，问想拿MCMC估计SV，但理解不了那个超长的函数，我就说这个叫做后验概率密度，他说什么叫后验密度？我说是通过贝叶斯原理得到的，他说贝叶斯不是统计学的吗？我要估计的是SV模型……尽最快速度达到目标，却忽略了积累与思索，这便得不偿失了。如果有负责的老师在这方面加以引导和帮助，我想这便是孩子们在大学，甚至是近十年的一大幸事。可惜的是这可遇而不可求，因为老师同样面临和孩子们一样的境况，只不过从保研变成了评职称而已，并且他们只能靠自己。
如果真的不想要培养研究人才，拜托请不要变相用这种方式折磨孩子们的思维了。至少在绝大多数工作中，老板不会让你没事就去研究MCMC……而在看过评论之后唯一欣慰的是，COS的编辑们很热心负责的，他们抱着对统计认真的态度，在帮助在这条路上迷乱的人，尽管这很有限，但也让很多人满载而归了。
深夜吐槽 尽请见谅~那我也来做点有用的吧。SV不多说，R上面也有包可以做，MCMC方法cos上也有文章介绍。如果你确实初学，没接触R也不想学非要用WinBUGS，这里有个paper
http://www.mysmu.edu/faculty/yujun/Research/YuEJ2000.pdf
我觉得这个东西，结合上面文章的内容，绝大多SV问题就应该解决了。另外有其他问题可以到cos论坛金融区去请教版主，版主在这方面可是很强的。
国内的金融方面最近几年一直很热，学生极多。很多学校会对金融的学生要求论文，而高质量的也是学生保研，各种评优的重要依据。最快的方法，就是用各种统计和计量方法来让自己的文章升级（记得有个神文写的是如何拿计量经济五大杀器力克CSSCI）。我接触的一些研究生，甚至是本科生，都在搞一些很高级的处理方法和模型（高级的意思是读一两本书都搞不清楚）。金融方面比较好用的，无非就是波动模型，因此在GARCH模型已经搞烂掉的现在，SV模型凭借它”拒人于千里之外“的难理解的特点越来越受到学生的青睐。这里的MCMC之所以被提如此之多，就是因为目前SV估计比较好的方法，就是它了。
有时候一想起这些就会有些郁闷，但觉得孩子们本身没错儿，靠自己的努力和智力，迅速学一些别人学不会的方法来争取自己的前程是很棒的。令我郁闷的是这种特别的规则而引发的浮躁心态以及对思考问题的态度。就拿SV来说，有个孩子学经济的，问想拿MCMC估计SV，但理解不了那个超长的函数，我就说这个叫做后验概率密度，他说什么叫后验密度？我说是通过贝叶斯原理得到的，他说贝叶斯不是统计学的吗？我要估计的是SV模型……尽最快速度达到目标，却忽略了积累与思索，这便得不偿失了。如果有负责的老师在这方面加以引导和帮助，我想这便是孩子们在大学，甚至是近十年的一大幸事。可惜的是这可遇而不可求，因为老师同样面临和孩子们一样的境况，只不过从保研变成了评职称而已，并且他们只能靠自己。
如果真的不想要培养研究人才，拜托请不要变相用这种方式折磨孩子们的思维了。至少在绝大多数工作中，老板不会让你没事就去研究MCMC……而在看过评论之后唯一欣慰的是，COS的编辑们很热心负责的，他们抱着对统计认真的态度，在帮助在这条路上迷乱的人，尽管这很有限，但也让很多人满载而归了。
深夜吐槽 尽请见谅~那我也来做点有用的吧。SV不多说，R上面也有包可以做，MCMC方法cos上也有文章介绍。如果你确实初学，没接触R也不想学非要用WinBUGS，这里有个paper
http://www.mysmu.edu/faculty/yujun/Research/YuEJ2000.pdf
我觉得这个东西，结合上面文章的内容，绝大多SV问题就应该解决了。另外有其他问题可以到cos论坛金融区去请教版主，版主在这方面可是很强的。
[未知用户] 神评论:)