| Stan Math Library 2.6.3 probability, sampling & optimization

- Main Page
- Namespaces
- Classes
- Files

- File List
- File Members

All Classes Namespaces Files Functions Variables Typedefs Enumerator Friends Macros
gaussian_dlm_obs_log.hpp
Go to the documentation of this file.
1 #ifndef STAN_MATH_PRIM_MAT_PROB_GAUSSIAN_DLM_OBS_LOG_HPP
2 #define STAN_MATH_PRIM_MAT_PROB_GAUSSIAN_DLM_OBS_LOG_HPP
3
4 #include <boost/random/normal_distribution.hpp>
5 #include <boost/random/variate_generator.hpp>
6 #include <stan/math/prim/mat/err/check_cov_matrix.hpp>
7 #include <stan/math/prim/scal/err/check_size_match.hpp>
8 #include <stan/math/prim/mat/err/check_spsd_matrix.hpp>
9 #include <stan/math/prim/scal/err/check_finite.hpp>
10 #include <stan/math/prim/scal/err/check_nonnegative.hpp>
11 #include <stan/math/prim/scal/err/check_not_nan.hpp>
12 #include <stan/math/prim/mat/fun/add.hpp>
13 #include <stan/math/prim/mat/fun/dot_product.hpp>
14 #include <stan/math/prim/mat/fun/inverse_spd.hpp>
15 #include <stan/math/prim/mat/fun/log.hpp>
16 #include <stan/math/prim/mat/fun/log_determinant_spd.hpp>
17 #include <stan/math/prim/mat/fun/multiply.hpp>
18 #include <stan/math/prim/mat/fun/quad_form.hpp>
19 #include <stan/math/prim/mat/fun/quad_form_sym.hpp>
20 #include <stan/math/prim/mat/fun/subtract.hpp>
21 #include <stan/math/prim/mat/fun/tcrossprod.hpp>
22 #include <stan/math/prim/mat/fun/trace_quad_form.hpp>
23 #include <stan/math/prim/mat/fun/transpose.hpp>
24
25 #include <stan/math/prim/scal/fun/constants.hpp>
26 #include <stan/math/prim/scal/meta/include_summand.hpp>
27
28 /*
29 TODO: time-varying system matrices
30 TODO: use sequential processing even for non-diagonal obs
31 covariance.
32 TODO: add constant terms in observation.
33 */
34
35 namespace stan {
36 namespace math {
70 template <bool propto,
71 typename T_y,
72 typename T_F, typename T_G,
73 typename T_V, typename T_W,
74 typename T_m0, typename T_C0
75 >
76 typename return_type<
77 T_y,
78 typename return_type<T_F, T_G, T_V, T_W, T_m0, T_C0>::type >::type
79 gaussian_dlm_obs_log(const Eigen::Matrix
80 <T_y, Eigen::Dynamic, Eigen::Dynamic>& y,
81 const Eigen::Matrix
82 <T_F, Eigen::Dynamic, Eigen::Dynamic>& F,
83 const Eigen::Matrix
84 <T_G, Eigen::Dynamic, Eigen::Dynamic>& G,
85 const Eigen::Matrix
86 <T_V, Eigen::Dynamic, Eigen::Dynamic>& V,
87 const Eigen::Matrix
88 <T_W, Eigen::Dynamic, Eigen::Dynamic>& W,
89 const Eigen::Matrix<T_m0, Eigen::Dynamic, 1>& m0,
90 const Eigen::Matrix
91 <T_C0, Eigen::Dynamic, Eigen::Dynamic>& C0) {
92 static const char* function("stan::math::gaussian_dlm_obs_log");
93 typedef typename return_type<
94 T_y,
95 typename return_type<T_F, T_G, T_V, T_W, T_m0, T_C0>::type>::type T_lp;
96 T_lp lp(0.0);
97
98 using stan::math::add;
99 using stan::math::check_cov_matrix;
100 using stan::math::check_finite;
101 using stan::math::check_not_nan;
102 using stan::math::check_size_match;
103 using stan::math::check_spsd_matrix;
104 using stan::math::check_square;
105 using stan::math::inverse_spd;
106 using stan::math::log_determinant_spd;
107 using stan::math::multiply;
108 using stan::math::quad_form_sym;
109 using stan::math::subtract;
110 using stan::math::trace_quad_form;
111 using stan::math::transpose;
112
113 int r = y.rows(); // number of variables
114 int T = y.cols(); // number of observations
115 int n = G.rows(); // number of states
116
117 // check y
118 check_finite(function, "y", y);
119 check_not_nan(function, "y", y);
120 // check F
121 check_size_match(function,
122 "columns of F", F.cols(),
123 "rows of y", y.rows());
124 check_size_match(function,
125 "rows of F", F.rows(),
126 "rows of G", G.rows());
127 check_finite(function, "F", F);
128 // check G
129 check_square(function, "G", G);
130 check_finite(function, "G", G);
131 // check V
132 check_size_match(function,
133 "rows of V", V.rows(),
134 "rows of y", y.rows());
135 // TODO(anyone): incorporate support for infinite V
136 check_finite(function, "V", V);
137 check_spsd_matrix(function, "V", V);
138 // check W
139 check_size_match(function,
140 "rows of W", W.rows(),
141 "rows of G", G.rows());
142 // TODO(anyone): incorporate support for infinite W
143 check_finite(function, "W", W);
144 check_spsd_matrix(function, "W", W);
145 // check m0
146 check_size_match(function,
147 "size of m0", m0.size(),
148 "rows of G", G.rows());
149 check_finite(function, "m0", m0);
150 // check C0
151 check_size_match(function,
152 "rows of C0", C0.rows(),
153 "rows of G", G.rows());
154 check_cov_matrix(function, "C0", C0);
155 check_finite(function, "C0", C0);
156
157 if (y.cols() == 0 || y.rows() == 0)
158 return lp;
159
160 if (include_summand<propto>::value) {
161 lp -= 0.5 * LOG_TWO_PI * r * T;
162 }
163
164 if (include_summand<propto, T_y, T_F, T_G, T_V, T_W, T_m0, T_C0>::value) {
165 Eigen::Matrix<T_lp, Eigen::Dynamic, 1> m(n);
166 Eigen::Matrix<T_lp, Eigen::Dynamic, Eigen::Dynamic> C(n, n);
167
168 // TODO(anyone): how to recast matrices
169 for (int i = 0; i < m0.size(); i++) {
170 m(i) = m0(i);
171 }
172 for (int i = 0; i < C0.rows(); i++) {
173 for (int j = 0; j < C0.cols(); j++) {
174 C(i, j) = C0(i, j);
175 }
176 }
177
178 Eigen::Matrix<typename return_type<T_y>::type,
179 Eigen::Dynamic, 1> yi(r);
180 Eigen::Matrix<T_lp, Eigen::Dynamic, 1> a(n);
181 Eigen::Matrix<T_lp, Eigen::Dynamic, Eigen::Dynamic> R(n, n);
182 Eigen::Matrix<T_lp, Eigen::Dynamic, 1> f(r);
183 Eigen::Matrix<T_lp, Eigen::Dynamic, Eigen::Dynamic> Q(r, r);
184 Eigen::Matrix<T_lp, Eigen::Dynamic, Eigen::Dynamic> Q_inv(r, r);
185 Eigen::Matrix<T_lp, Eigen::Dynamic, 1> e(r);
186 Eigen::Matrix<T_lp, Eigen::Dynamic, Eigen::Dynamic> A(n, r);
187
188 for (int i = 0; i < y.cols(); i++) {
189 yi = y.col(i);
190 // // Predict state
191 // a_t = G_t m_{t-1}
192 a = multiply(G, m);
193 // R_t = G_t C_{t-1} G_t' + W_t
194 R = add(quad_form_sym(C, transpose(G)), W);
195 // // predict observation
196 // f_t = F_t' a_t
197 f = multiply(transpose(F), a);
198 // Q_t = F'_t R_t F_t + V_t
199 Q = add(quad_form_sym(R, F), V);
200 Q_inv = inverse_spd(Q);
201 // // filtered state
202 // e_t = y_t - f_t
203 e = subtract(yi, f);
204 // A_t = R_t F_t Q^{-1}_t
205 A = multiply(multiply(R, F), Q_inv);
206 // m_t = a_t + A_t e_t
207 m = add(a, multiply(A, e));
208 // C = R_t - A_t Q_t A_t'
209 C = subtract(R, quad_form_sym(Q, transpose(A)));
210 lp -= 0.5 * (log_determinant_spd(Q) + trace_quad_form(Q_inv, e));
211 }
212 }
213 return lp;
214 }
215
216 template <typename T_y,
217 typename T_F, typename T_G,
218 typename T_V, typename T_W,
219 typename T_m0, typename T_C0
220 >
221 inline
222 typename return_type<
223 T_y,
224 typename return_type<T_F, T_G, T_V, T_W, T_m0, T_C0>::type >::type
225 gaussian_dlm_obs_log(const Eigen::Matrix
226 <T_y, Eigen::Dynamic, Eigen::Dynamic>& y,
227 const Eigen::Matrix
228 <T_F, Eigen::Dynamic, Eigen::Dynamic>& F,
229 const Eigen::Matrix
230 <T_G, Eigen::Dynamic, Eigen::Dynamic>& G,
231 const Eigen::Matrix
232 <T_V, Eigen::Dynamic, Eigen::Dynamic>& V,
233 const Eigen::Matrix
234 <T_W, Eigen::Dynamic, Eigen::Dynamic>& W,
235 const Eigen::Matrix<T_m0, Eigen::Dynamic, 1>& m0,
236 const Eigen::Matrix
237 <T_C0, Eigen::Dynamic, Eigen::Dynamic>& C0) {
238 return gaussian_dlm_obs_log<false>(y, F, G, V, W, m0, C0);
239 }
240
276 template <bool propto,
277 typename T_y,
278 typename T_F, typename T_G,
279 typename T_V, typename T_W,
280 typename T_m0, typename T_C0
281 >
282 typename return_type<
283 T_y,
284 typename return_type<T_F, T_G, T_V, T_W, T_m0, T_C0>::type >::type
285 gaussian_dlm_obs_log(const Eigen::Matrix
286 <T_y, Eigen::Dynamic, Eigen::Dynamic>& y,
287 const Eigen::Matrix
288 <T_F, Eigen::Dynamic, Eigen::Dynamic>& F,
289 const Eigen::Matrix
290 <T_G, Eigen::Dynamic, Eigen::Dynamic>& G,
291 const Eigen::Matrix<T_V, Eigen::Dynamic, 1>& V,
292 const Eigen::Matrix
293 <T_W, Eigen::Dynamic, Eigen::Dynamic>& W,
294 const Eigen::Matrix<T_m0, Eigen::Dynamic, 1>& m0,
295 const Eigen::Matrix
296 <T_C0, Eigen::Dynamic, Eigen::Dynamic>& C0) {
297 static const char* function("stan::math::gaussian_dlm_obs_log");
298 typedef
299 typename return_type
300 <T_y, typename return_type<T_F, T_G, T_V, T_W, T_m0, T_C0>::type>::type
301 T_lp;
302 T_lp lp(0.0);
303
304 using stan::math::add;
305 using stan::math::check_cov_matrix;
306 using stan::math::check_finite;
307 using stan::math::check_nonnegative;
308 using stan::math::check_not_nan;
309 using stan::math::check_size_match;
310 using stan::math::check_spsd_matrix;
311 using stan::math::dot_product;
312 using stan::math::multiply;
313 using stan::math::quad_form_sym;
314 using stan::math::subtract;
315 using stan::math::tcrossprod;
316 using stan::math::trace_quad_form;
317 using stan::math::transpose;
318 using std::log;
319
320 int r = y.rows(); // number of variables
321 int T = y.cols(); // number of observations
322 int n = G.rows(); // number of states
323
324 // check y
325 check_finite(function, "y", y);
326 check_not_nan(function, "y", y);
327 // check F
328 check_size_match(function,
329 "columns of F", F.cols(),
330 "rows of y", y.rows());
331 check_size_match(function,
332 "rows of F", F.rows(),
333 "rows of G", G.rows());
334 check_finite(function, "F", F);
335 check_not_nan(function, "F", F);
336 // check G
337 check_size_match(function,
338 "rows of G", G.rows(),
339 "columns of G", G.cols());
340 check_finite(function, "G", G);
341 check_not_nan(function, "G", G);
342 // check V
343 check_nonnegative(function, "V", V);
344 check_size_match(function,
345 "size of V", V.size(),
346 "rows of y", y.rows());
347 // TODO(anyone): support infinite V
348 check_finite(function, "V", V);
349 check_not_nan(function, "V", V);
350 // check W
351 check_spsd_matrix(function, "W", W);
352 check_size_match(function,
353 "rows of W", W.rows(),
354 "rows of G", G.rows());
355 // TODO(anyone): support infinite W
356 check_finite(function, "W", W);
357 check_not_nan(function, "W", W);
358 // check m0
359 check_size_match(function,
360 "size of m0", m0.size(),
361 "rows of G", G.rows());
362 check_finite(function, "m0", m0);
363 check_not_nan(function, "m0", m0);
364 // check C0
365 check_cov_matrix(function, "C0", C0);
366 check_size_match(function,
367 "rows of C0", C0.rows(),
368 "rows of G", G.rows());
369 check_finite(function, "C0", C0);
370 check_not_nan(function, "C0", C0);
371
372 if (y.cols() == 0 || y.rows() == 0)
373 return lp;
374
375 if (include_summand<propto>::value) {
376 lp += 0.5 * NEG_LOG_TWO_PI * r * T;
377 }
378
379 if (include_summand<propto, T_y, T_F, T_G, T_V, T_W, T_m0, T_C0>::value) {
380 T_lp f;
381 T_lp Q;
382 T_lp Q_inv;
383 T_lp e;
384 Eigen::Matrix<T_lp, Eigen::Dynamic, 1> A(n);
385 Eigen::Matrix<T_lp, Eigen::Dynamic, 1> Fj(n);
386 Eigen::Matrix<T_lp, Eigen::Dynamic, 1> m(n);
387 Eigen::Matrix<T_lp, Eigen::Dynamic, Eigen::Dynamic> C(n, n);
388
389 // TODO(anyone): how to recast matrices
390 for (int i = 0; i < m0.size(); i++) {
391 m(i) = m0(i);
392 }
393 for (int i = 0; i < C0.rows(); i++) {
394 for (int j = 0; j < C0.cols(); j++) {
395 C(i, j) = C0(i, j);
396 }
397 }
398
399 for (int i = 0; i < y.cols(); i++) {
400 // Predict state
401 // reuse m and C instead of using a and R
402 m = multiply(G, m);
403 C = add(quad_form_sym(C, transpose(G)), W);
404 for (int j = 0; j < y.rows(); ++j) {
405 // predict observation
406 T_lp yij(y(j, i));
407 // dim Fj = (n, 1)
408 for (int k = 0; k < F.rows(); ++k) {
409 Fj(k) = F(k, j);
410 }
411 // // f_{t, i} = F_{t, i}' m_{t, i-1}
412 f = dot_product(Fj, m);
413 Q = trace_quad_form(C, Fj) + V(j);
414 Q_inv = 1.0 / Q;
415 // // filtered observation
416 // // e_{t, i} = y_{t, i} - f_{t, i}
417 e = yij - f;
418 // // A_{t, i} = C_{t, i-1} F_{t, i} Q_{t, i}^{-1}
419 A = multiply(multiply(C, Fj), Q_inv);
420 // // m_{t, i} = m_{t, i-1} + A_{t, i} e_{t, i}
421 m += multiply(A, e);
422 // // c_{t, i} = C_{t, i-1} - Q_{t, i} A_{t, i} A_{t, i}'
423 // // // tcrossprod throws an error (ambiguous)
424 // C = subtract(C, multiply(Q, tcrossprod(A)));
425 C -= multiply(Q, multiply(A, transpose(A)));
426 C = 0.5 * add(C, transpose(C));
427 lp -= 0.5 * (log(Q) + pow(e, 2) * Q_inv);
428 }
429 }
430 }
431 return lp;
432 }
433
434 template <typename T_y,
435 typename T_F, typename T_G,
436 typename T_V, typename T_W,
437 typename T_m0, typename T_C0>
438 inline
439 typename return_type
440 <T_y, typename return_type<T_F, T_G, T_V, T_W, T_m0, T_C0>::type>::type
441 gaussian_dlm_obs_log
442 (const Eigen::Matrix<T_y, Eigen::Dynamic, Eigen::Dynamic>& y,
443 const Eigen::Matrix<T_F, Eigen::Dynamic, Eigen::Dynamic>& F,
444 const Eigen::Matrix<T_G, Eigen::Dynamic, Eigen::Dynamic>& G,
445 const Eigen::Matrix<T_V, Eigen::Dynamic, 1>& V,
446 const Eigen::Matrix<T_W, Eigen::Dynamic, Eigen::Dynamic>& W,
447 const Eigen::Matrix<T_m0, Eigen::Dynamic, 1>& m0,
448 const Eigen::Matrix<T_C0, Eigen::Dynamic, Eigen::Dynamic>& C0) {
449 return gaussian_dlm_obs_log<false>(y, F, G, V, W, m0, C0);
450 }
451 }
452
453 }
454
455 #endif
check_not_nan.hpp
multiply.hpp
tcrossprod.hpp
log_determinant_spd.hpp
stan::math::check_not_nan
bool check_not_nan(const char *function, const char *name, const T_y &y)
Return true if y is not NaN.
Definition: check_not_nan.hpp:63
check_size_match.hpp
stan::math::subtract
Eigen::Matrix< typename boost::math::tools::promote_args< T1, T2 >::type, R, C > subtract(const Eigen::Matrix< T1, R, C > &m1, const Eigen::Matrix< T2, R, C > &m2)
Return the result of subtracting the second specified matrix from the first specified matrix...
Definition: subtract.hpp:27
stan::math::gaussian_dlm_obs_log
return_type< T_y, typename return_type< T_F, T_G, T_V, T_W, T_m0, T_C0 >::type >::type gaussian_dlm_obs_log(const Eigen::Matrix< T_y, Eigen::Dynamic, Eigen::Dynamic > &y, const Eigen::Matrix< T_F, Eigen::Dynamic, Eigen::Dynamic > &F, const Eigen::Matrix< T_G, Eigen::Dynamic, Eigen::Dynamic > &G, const Eigen::Matrix< T_V, Eigen::Dynamic, Eigen::Dynamic > &V, const Eigen::Matrix< T_W, Eigen::Dynamic, Eigen::Dynamic > &W, const Eigen::Matrix< T_m0, Eigen::Dynamic, 1 > &m0, const Eigen::Matrix< T_C0, Eigen::Dynamic, Eigen::Dynamic > &C0)
The log of a Gaussian dynamic linear model (GDLM).
Definition: gaussian_dlm_obs_log.hpp:79
stan::math::log
fvar< T > log(const fvar< T > &x)
Definition: log.hpp:15
stan::math::multiply
Eigen::Matrix< fvar< T >, R1, C1 > multiply(const Eigen::Matrix< fvar< T >, R1, C1 > &m, const fvar< T > &c)
Definition: multiply.hpp:20
add.hpp
stan::return_type
Metaprogram to calculate the base scalar return type resulting from promoting all the scalar types of...
Definition: return_type.hpp:19
stan::math::check_cov_matrix
bool check_cov_matrix(const char *function, const char *name, const Eigen::Matrix< T_y, Dynamic, Dynamic > &y)
Return true if the specified matrix is a valid covariance matrix.
Definition: check_cov_matrix.hpp:32
stan::math::tcrossprod
Eigen::Matrix< fvar< T >, R, R > tcrossprod(const Eigen::Matrix< fvar< T >, R, C > &m)
Definition: tcrossprod.hpp:17
stan::math::include_summand
Template metaprogram to calculate whether a summand needs to be included in a proportional (log) prob...
Definition: include_summand.hpp:36
log.hpp
check_cov_matrix.hpp
stan::math::quad_form_sym
Eigen::Matrix< T, CB, CB > quad_form_sym(const Eigen::Matrix< T, RA, CA > &A, const Eigen::Matrix< T, RB, CB > &B)
Definition: quad_form_sym.hpp:19
transpose.hpp
stan::return_type::type
boost::math::tools::promote_args< typename scalar_type< T1 >::type, typename scalar_type< T2 >::type, typename scalar_type< T3 >::type, typename scalar_type< T4 >::type, typename scalar_type< T5 >::type, typename scalar_type< T6 >::type >::type type
Definition: return_type.hpp:27
include_summand.hpp
stan::math::LOG_TWO_PI
const double LOG_TWO_PI
Definition: constants.hpp:193
check_nonnegative.hpp
stan::math::trace_quad_form
stan::math::fvar< T > trace_quad_form(const Eigen::Matrix< stan::math::fvar< T >, RA, CA > &A, const Eigen::Matrix< stan::math::fvar< T >, RB, CB > &B)
Definition: trace_quad_form.hpp:18
quad_form_sym.hpp
trace_quad_form.hpp
stan::math::check_size_match
bool check_size_match(const char *function, const char *name_i, T_size1 i, const char *name_j, T_size2 j)
Return true if the provided sizes match.
Definition: check_size_match.hpp:30
stan::math::dot_product
fvar< T > dot_product(const Eigen::Matrix< fvar< T >, R1, C1 > &v1, const Eigen::Matrix< fvar< T >, R2, C2 > &v2)
Definition: dot_product.hpp:20
dot_product.hpp
stan::math::check_spsd_matrix
bool check_spsd_matrix(const char *function, const char *name, const Eigen::Matrix< T_y, Dynamic, Dynamic > &y)
Return true if the specified matrix is a square, symmetric, and positive semi-definite.
Definition: check_spsd_matrix.hpp:32
stan::math::e
double e()
Return the base of the natural logarithm.
Definition: constants.hpp:95
stan::math::check_finite
bool check_finite(const char *function, const char *name, const T_y &y)
Return true if y is finite.
Definition: check_finite.hpp:62
constants.hpp
check_finite.hpp
stan::math::inverse_spd
Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic > inverse_spd(const Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic > &m)
Returns the inverse of the specified symmetric, pos/neg-definite matrix.
Definition: inverse_spd.hpp:19
stan::math::NEG_LOG_TWO_PI
const double NEG_LOG_TWO_PI
Definition: constants.hpp:195
stan::math::add
Eigen::Matrix< typename boost::math::tools::promote_args< T1, T2 >::type, R, C > add(const Eigen::Matrix< T1, R, C > &m1, const Eigen::Matrix< T2, R, C > &m2)
Return the sum of the specified matrices.
Definition: add.hpp:27
stan::math::pow
fvar< T > pow(const fvar< T > &x1, const fvar< T > &x2)
Definition: pow.hpp:18
stan::math::check_nonnegative
bool check_nonnegative(const char *function, const char *name, const T_y &y)
Return true if y is non-negative.
Definition: check_nonnegative.hpp:66
stan::math::transpose
Eigen::Matrix< T, C, R > transpose(const Eigen::Matrix< T, R, C > &m)
Definition: transpose.hpp:12
quad_form.hpp
subtract.hpp
stan::math::check_square
bool check_square(const char *function, const char *name, const Eigen::Matrix< T_y, Eigen::Dynamic, Eigen::Dynamic > &y)
Return true if the specified matrix is square.
Definition: check_square.hpp:28
check_spsd_matrix.hpp
inverse_spd.hpp
stan::math::log_determinant_spd
T log_determinant_spd(const Eigen::Matrix< T, R, C > &m)
Returns the log absolute determinant of the specified square matrix.
Definition: log_determinant_spd.hpp:19

[ Stan Home Page ] © 2011–2015, Stan Development Team.