124 lines
2.8 KiB
Plaintext
124 lines
2.8 KiB
Plaintext
|
@q $Id$ @>
|
||
|
@q Copyright 2007, Ondra Kamenik @>
|
||
|
|
||
|
@ Start of {\tt normal\_conjugate.cpp} file.
|
||
|
|
||
|
@c
|
||
|
|
||
|
#include "normal_conjugate.h"
|
||
|
#include "kord_exception.h"
|
||
|
|
||
|
@<|NormalConj| diffuse prior constructor@>;
|
||
|
@<|NormalConj| data update constructor@>;
|
||
|
@<|NormalConj| copy constructor@>;
|
||
|
@<|NormalConj::update| one observation code@>;
|
||
|
@<|NormalConj::update| multiple observations code@>;
|
||
|
@<|NormalConj::update| with |NormalConj| code@>;
|
||
|
@<|NormalConj::getVariance| code@>;
|
||
|
|
||
|
@
|
||
|
@<|NormalConj| diffuse prior constructor@>=
|
||
|
NormalConj::NormalConj(int d)
|
||
|
: mu(d), kappa(0), nu(-1), lambda(d,d)
|
||
|
{
|
||
|
mu.zeros();
|
||
|
lambda.zeros();
|
||
|
}
|
||
|
|
||
|
@
|
||
|
@<|NormalConj| data update constructor@>=
|
||
|
NormalConj::NormalConj(const ConstTwoDMatrix& ydata)
|
||
|
: mu(ydata.numRows()), kappa(ydata.numCols()), nu(ydata.numCols()-1),
|
||
|
lambda(ydata.numRows(), ydata.numRows())
|
||
|
{
|
||
|
mu.zeros();
|
||
|
for (int i = 0; i < ydata.numCols(); i++)
|
||
|
mu.add(1.0/ydata.numCols(), ConstVector(ydata, i));
|
||
|
|
||
|
lambda.zeros();
|
||
|
for (int i = 0; i < ydata.numCols(); i++) {
|
||
|
Vector diff(ConstVector(ydata, i));
|
||
|
diff.add(-1, mu);
|
||
|
lambda.addOuter(diff);
|
||
|
}
|
||
|
}
|
||
|
|
||
|
@
|
||
|
@<|NormalConj| copy constructor@>=
|
||
|
NormalConj::NormalConj(const NormalConj& nc)
|
||
|
: mu(nc.mu), kappa(nc.kappa), nu(nc.nu), lambda(nc.lambda)
|
||
|
{
|
||
|
}
|
||
|
|
||
|
@ The method performs the following:
|
||
|
$$\eqalign{
|
||
|
\mu_1 = &\; {\kappa_0\over \kappa_0+1}\mu_0 + {1\over \kappa_0+1}y\cr
|
||
|
\kappa_1 = &\; \kappa_0 + 1\cr
|
||
|
\nu_1 = &\; \nu_0 + 1\cr
|
||
|
\Lambda_1 = &\; \Lambda_0 + {\kappa_0\over\kappa_0+1}(y-\mu_0)(y-\mu_0)^T,
|
||
|
}$$
|
||
|
|
||
|
@<|NormalConj::update| one observation code@>=
|
||
|
void NormalConj::update(const ConstVector& y)
|
||
|
{
|
||
|
KORD_RAISE_IF(y.length() != mu.length(),
|
||
|
"Wrong length of a vector in NormalConj::update");
|
||
|
|
||
|
mu.mult(kappa/(1.0+kappa));
|
||
|
mu.add(1.0/(1.0+kappa), y);
|
||
|
|
||
|
Vector diff(y);
|
||
|
diff.add(-1, mu);
|
||
|
lambda.addOuter(diff, kappa/(1.0+kappa));
|
||
|
|
||
|
kappa++;
|
||
|
nu++;
|
||
|
}
|
||
|
|
||
|
@ The method evaluates the formula in the header file.
|
||
|
|
||
|
@<|NormalConj::update| multiple observations code@>=
|
||
|
void NormalConj::update(const ConstTwoDMatrix& ydata)
|
||
|
{
|
||
|
NormalConj nc(ydata);
|
||
|
update(nc);
|
||
|
}
|
||
|
|
||
|
|
||
|
@
|
||
|
@<|NormalConj::update| with |NormalConj| code@>=
|
||
|
void NormalConj::update(const NormalConj& nc)
|
||
|
{
|
||
|
double wold = ((double)kappa)/(kappa+nc.kappa);
|
||
|
double wnew = 1-wold;
|
||
|
|
||
|
mu.mult(wold);
|
||
|
mu.add(wnew, nc.mu);
|
||
|
|
||
|
Vector diff(nc.mu);
|
||
|
diff.add(-1, mu);
|
||
|
lambda.add(1.0, nc.lambda);
|
||
|
lambda.addOuter(diff);
|
||
|
|
||
|
kappa = kappa + nc.kappa;
|
||
|
nu = nu + nc.kappa;
|
||
|
}
|
||
|
|
||
|
|
||
|
@ This returns ${1\over \nu-d-1}\Lambda$, which is the mean of the
|
||
|
variance in the posterior distribution. If the number of degrees of
|
||
|
freedom is less than $d$, then NaNs are returned.
|
||
|
|
||
|
@<|NormalConj::getVariance| code@>=
|
||
|
void NormalConj::getVariance(TwoDMatrix& v) const
|
||
|
{
|
||
|
if (nu > getDim()+1) {
|
||
|
v = (const TwoDMatrix&)lambda;
|
||
|
v.mult(1.0/(nu-getDim()-1));
|
||
|
} else
|
||
|
v.nans();
|
||
|
}
|
||
|
|
||
|
|
||
|
@ End of {\tt normal\_conjugate.cpp} file.
|