Changeset 180

Timestamp:

05/15/08 01:27:07 (7 months ago)

Author:

edsuom

Message:

Grinding along with C code for new model

Files:

• projects/AsynCluster/trunk/svpmc/model.c (modified) (9 diffs)

projects/AsynCluster/trunk/svpmc/model.c

@@ -90 +90 @@
-struct parameters
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-single normal variate and the log-volatility sample derived from it
+proposal sample
-struct sample
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-};
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@@ -132 +145 @@
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+// Log probability density of the unit normal distribution
+double normLogDensity(double x)
+{
+  static double logC = -log(2 * 3.1415926535897931);
+  return -0.5*x*x + logC;
+}
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-// Matrix multiplication x*y -> +z
-//
@@ -153 +174 @@
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-  int k;
@@ -170 +187 @@
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-  // Start with the matrix product for the VAR(1) term...
-h0, S->h
+S->h, S->x0
-  // then compute and add the multivariate volatility shock given the proposed
-  // normal variate...
-_zv, S->z, S->h
+z, S->z, S->x0
-  // ...plus the multivariate volatility offset
-  for(k=0; k<N; k++)
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-}
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@@ -182 +203 @@
-// Computes and returns the log-likelihood of a multivariate innovation x given
-// a 1-D array containing a multivariate log-volatility.
-double
+void
-{
-  int k;
@@ -189 +210 @@
-  double L = 0;
-  const int N = (int) P->d->dimensions[0];
-  y = (double *) malloc(2*N * sizeof(double));
-
-  // Inverse-scale the innovation by the square root of the volatilities in
-  // preparation for decorrelation
-  for(k=0; k<N; k++)
-PA1(y
+SPA1(S, x0
-    
-  // Now decorrelate the equi-variance innovations in preparation for the
-  // log-likelihood computation for this multivariate log-volatility proposal
-_y, y, y+N
+, S->x0, S->x1
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-  // Now compute log Pr(x[t]|h[t]), the log-likelihood of the decorrelated
@@ -210 +230 @@
-    // shocks, times the value of the normal variate value underlying the
-    // volatility shock for this sample
-PA1(y, k) = PA1(y, k+N
+SPA1(S, x0, k) = SPA1(S, x1, k
-    // The log-likelihood for this sample is simply the argument of the
-    // exponential of the normal pdf, with the reciprocal scaling of the
-    // exponential being done in log space by subtraction of the log of the
-    // scaling coefficient
-PA1(y
+SPA1(S, x0
-    L -= 0.5 * val*val + SPP2(P, c, k, 1); // Fast squaring instead of pow()
-    // Since the innovation has been inverse-scaled by the square root of the
@@ -224 +244 @@
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-  // All done
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@@ -234 +273 @@
-// ----------------------------------------------------------------------------
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-array
+variable
-// z    Independent normal variates, [pn] array (updated)
-// h    Simulated last-sample multivariate log-volatility, [p] (overwritten)
-// x    Innovation values, [pn] array
-ri   Inverse, concurrent cross-correlations between innovation shocks, [pp]
-c    Constants for multivariate normal PDF
-
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+w    Wiggle value for +/- proposals (float)
+n    Number of proposals to try per sample (int)
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+, direct
-// d    Volatility offset, [p] vector
-// e    Lag-1 cross-correlations for VAR of volatilities, [pp] array
-// g    Volatility/innovation shock correlations, [p] vector
-
+//--- Model parameters, derivations ---
+// rz   Concurrent xcorr, innovation-volatility normals, [pp]
+// ri   Inverse concurrent xcorr, innovation shocks, [pp]
+// c    Constants for multivariate normal PDF
+
-//--- Return values (tuple) ---
-// 0    Lx: Log-likelihood of innovations given simulated log-volatilities
-// 1    Lh: Log-likelihood of simulated log-volatilities
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-py::tuple result(2);
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+// Generate parameter struct
+P.d = d_array; P.e = e_array; P.g = g_array;
+P.rz = rz_array; P.ri = ri_array; P.c = c_array;
+
+// Initialize various arrays
+x0 = (double *) calloc(sizeof(double), Nt);
+for(k0=0; k0<n; k0++) {
+  sp[k0].h = (double *) calloc(sizeof(double), Nt);
+  sp[k0].x0 = (double *) calloc(sizeof(double), Nt);
+  sp[k0].x1 = (double *) calloc(sizeof(double), Nt);
+}
+
-// For each sample...
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-}
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-// All done
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-return_val = result;