Matlab用BUGS马尔可夫区制转换Markov switching随机波动率模型、序列蒙特卡罗SMC、M H采样分析时间序列

file = 'ssv.bug'; % BUGS模型文件名

model
{
  x\[1\] ~ dnorm(mm\[1\], 1/sig^2)
  y\[1\] ~ dnorm(0, exp(-x\[1\]))

  for (t in 2:tmax)
  {
    c\[t\] ~ dcat(ifelse(c\[t-1\]==1, pi\[1,\], pi\[2,\]))
    mm\[t\] <- alp\[1\] * (c\[t\]==1) + alp\[2\]*(c\[t\]==2) + ph*x\[t-1\]

addpath(path)

lightblue 
lightred 

% 设置随机数生成器的种子以实现可重复性
if eLan 'matlab', '7.2')
    rnd('state', 0)
else
    rng('default')
end

tmax = 100;
sig = .4;

model(file, data, 'sample', true);
data = model;

figure('nae', 'Lrtrs')
plot(1:tmax, dt.y)

n_part = 5000; % 粒子数
  {'x'}; % 要监控的变量
 smc =  samples（npart）；

diag   (smc);

sem(ess)

plot(1:tmax, 30*(tmax,1), '--k')

for ttt=1:tttmax
    va = unique(outtt.x.s.vaues(ttt,:));

    wegh = arrayfun(@(x) sum(outtt.x.s.weittt(ttt, outtt.x.s.vaues(ttt,:) == x)), va);

    scatttttter(ttt\*ones(size(va)), va, min(50, .5\*n_parttt*wegh), 'r',...
        'markerf', 'r')
end

summary(out, 'pro', \[.025, .975\]);

mean = susmc.x.f.mean;
xfqu = susmc.x.f.quant;
h = fill(\[1:tmax, tmax:-1:1\], \[xfqu{1}; flipud(xfqu{2})\], 0);

plot(1:tmax, mean,)
plot(1:tmax, data.x_true)

mean = smcx.s.mean;
quant = smcx.s.quant;

plot(1:t_max, mean,  3)
plot(1:t\_max, data.x\_true, 'g')

kde = density(out);
for k=1:numel(time)
    tk = time(k);
    plot(kde.x.f(tk).x, kde.x.f(tk).f);
    hold on
    plot(kde.x.s(tk).x, kde.x.s(tk).f, 'r');
    plot(data.xtrue(tk));
    box off
end

thi= 1;
nprt = 50;

init(moel, vaibls);
upda(obj, urn, npat); % 预烧迭代
sample(obj,...
    nier, npat, 'thin', thn);

summary(out, 'prs');

mean = sumx.man;
quant = su.x.qunt;

hold on
plot(1:tax, man, 'r', 'liith', 3)
plot(1:tax, xrue, 'g')

for k=1:nmel(timndx)
    tk = tieinx(k);
    sublt(2, 2, k)
    plot(outm.x(tk, :), 'liedh', 1)
    hold on
    plot(0, d_retk), '*g');
    box off
end

for k=1:numel(tim_ix)
    tk = tim_ix(k);
    subplot(2, 2, k)
    hist(o_hx(tk, :), 20);
    h = fidobj(gca, 'ype, 'ptc');    hold on
    plot(daau(k), 0, '*g');
   
    box off
end

pmh = desity(otmh);
for k=1:numel(tenx)
    tk = tim_ix(k);
    subplot(2, 2, k)
    plot(x(t).x, dpi.x(tk).f, 'r');
    hold on
    plot(xtrue(tk), 0, '*g');
    box off
end

n= 50; % 粒子数
para = {'alpha}; % 我们要研究灵敏度的参数
 % 两个分量的值网格
pvs = {A(:, B(:';

smcs(modl, par, parvlu, npt);

surf(A, B, reshape(ouma_i, sizeA)
box off