Changeset 285 for bdm/stat

Timestamp:

03/05/09 14:03:33 (15 years ago)

Author:

smidl

Message:

unit-step experiment TR2245p

Files:

• bdm/stat/libEF.h (modified) (4 diffs)

bdm/stat/libEF.h

@@ -323 +323 @@
         //! \name Constructors
         //!@{
-        egamma ( ) :eEF ( ), alpha(), beta() {};
+        egamma ( ) :eEF ( ), alpha(0), beta(0) {};
         egamma ( const vec &a, const vec &b ) {set_parameters ( a, b );};
         void set_parameters ( const vec &a, const vec &b ) {alpha=a,beta=b;dim = alpha.length();};
@@ -629 +629 @@
 The standard deviation of the walk is then: \f$\mu/\sqrt(k)\f$.
  */
-class migamma_fix : public migamma {
+class migamma_ref : public migamma {
 protected:
         //! parameter l
@@ -637 +637 @@
 public:
         //! Constructor
-        migamma_fix ( ) : migamma (),refl ( ) {};
+        migamma_ref ( ) : migamma (),refl ( ) {};
         //! Set value of \c k
         void set_parameters ( double k0 , vec ref0, double l0 ) {
@@ -651 +651 @@
         };
 };
+
+/*! Log-Normal probability density
+ only allow diagonal covariances!
+
+Density of the form \f$ \log(x)\sim \mathcal{N}(\mu,\sigma^2), i.e.
+\f[
+x \sim \frac{1}{x\sigma\sqrt{2\pi}}\exp{-\frac{1}{2\sigma^2}(\log(x)-\mu)}
+\f]
+
+*/
+class elognorm: public enorm<ldmat>{
+        public:
+                vec sample() const {return exp(enorm<ldmat>::sample());};
+                vec mean() const {vec var=enorm<ldmat>::variance();return exp(mu - 0.5*var);};
+                
+};
+
+/*!
+\brief  Log-Normal random walk
+
+Mean value, \f$\mu\f$, is...
+
+==== Check == vv =
+Standard deviation of the random walk is proportional to one \f$k\f$-th the mean.
+This is achieved by setting \f$\alpha=k\f$ and \f$\beta=k/\mu\f$.
+
+The standard deviation of the walk is then: \f$\mu/\sqrt(k)\f$.
+ */
+class mlognorm : public mpdf {
+        protected:
+                elognorm eno;
+        //! parameter 1/2*sigma^2
+                double sig2;
+                //! access
+                vec &mu;
+        public:
+        //! Constructor
+                mlognorm ( ) : eno (), mu(eno._mu()) {ep=&eno;};
+        //! Set value of \c k
+                void set_parameters ( int size, double k) {
+                        sig2 = 0.5*log(k*k+1);
+                        eno.set_parameters(zeros(size),2*sig2*eye(size));
+                        
+                        dimc = size;
+                };
+
+                void condition ( const vec &val ) {
+                        mu=log(val)-sig2;//elem_mult ( refl,pow ( val,l ) );
+                };
+};
+
+/*! inverse Wishart density
+
+*/
+class iW : public epdf{
+        public:
+                vec sample(){}
+};
+
 //! Switch between various resampling methods.
 enum RESAMPLING_METHOD { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 };