我正在尝试使用 对多元高斯进行采样mvnrnd，但是当我运行时，样本是NaN. 例如 (mu是一个 1×50 的向量，Sigma是一个 50×50 的矩阵，并且M等于 50)：
Samp = mvnrnd(mu,Sigma,M)
当我运行它并检查Samp工作区中命令和变量的值时，它给了我NaN. 为什么？mvnrnd不适用于更大的尺寸？
* 编辑* 以下是 mu 的值：
mu =
Columns 1 through 15
14.3632 8.7442 -3.2029 -16.0259 -8.8725 -10.7675 9.2580 13.2556 -1.4326 -11.5970 1.9023 14.1793 9.9024 -12.7794 -0.2913
Columns 16 through 30
-4.3093 -15.2170 23.2225 4.5019 16.8539 0.6878 3.4329 1.9528 5.5965 0.1680 3.3895 15.1409 12.5796 11.8425 5.4299
Columns 31 through 45
-20.4517 12.3072 -7.5742 -14.9738 17.6118 -1.0414 3.2530 -0.1278 1.0009 3.2609 10.5319 -19.2158 -1.5661 -8.3426 -12.0159
Columns 46 through 50
6.3250 12.1056 11.5938 24.9523 -13.1602
西格玛的值：
1.0e+003 *
Columns 1 through 15
0.6526 -0.2398 0.2117 0.3901 -0.0241 0.1888 -0.2221 0.1173 -0.2380 -0.0327 0.0408 0.1229 0.0616 0.1475 -0.2264
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Columns 16 through 30
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Columns 31 through 45
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0.0389 -0.0814 -0.0763 0.0825 -0.2712 0.2013 0.6594 0.0057 -0.8038 0.1018 0.0286 0.1804 0.0083 -0.5634 -0.7327
0.1523 -0.3044 0.0445 -0.2515 -0.0906 -0.2194 -0.3834 -0.0915 0.4598 0.2295 0.0873 -0.2526 0.1777 0.3195 0.3820
0.0280 0.1419 0.0492 -0.1152 -0.2380 -0.1733 -0.0417 0.3662 0.1061 -0.0279 -0.0564 -0.0167 0.4917 0.0734 0.1499
0.0666 0.3170 0.1796 -0.2144 -0.2680 -0.1788 -0.0524 -0.0172 0.1232 -0.3269 -0.1634 0.0872 0.1050 0.2208 -0.1069
-0.2162 -0.0964 -0.0089 0.0347 -0.0291 0.0570 0.0248 0.1566 -0.4006 0.1315 -0.2309 0.1069 -0.0940 -0.0757 -0.3878
-0.3968 -0.0802 -0.1999 0.3181 -0.0343 0.0618 0.0544 0.2663 -0.5369 0.2755 -0.4115 0.0066 0.3602 -0.1881 -0.2722
-0.0698 -0.0655 0.1888 -0.1686 -0.3779 -0.1513 -0.2557 0.1528 -0.2308 0.2552 -0.0068 -0.0327 0.2356 0.1552 -0.2290
-0.0207 -0.1549 0.2938 0.0125 0.1065 0.1417 0.0243 0.1494 0.2989 0.0031 -0.0229 0.0130 0.1184 0.0119 0.0889
-0.0893 0.4400 -0.2767 0.0108 0.3992 -0.1844 -0.0554 -0.0407 0.3189 -0.5586 0.0189 0.0817 -0.2522 0.1321 0.3339
-0.0483 0.3391 0.1120 0.1503 0.2066 0.0846 -0.2783 0.0096 0.4289 -0.2286 -0.4013 0.0258 -0.0306 0.3246 0.2808
0.1385 0.0921 0.4970 -0.2403 -0.3149 0.0036 -0.2822 0.4694 -0.0620 0.0695 0.2605 0.1196 0.2737 0.0352 -0.1718
-0.0376 -0.2724 -0.0022 0.1669 -0.0089 0.1798 0.6476 -0.1303 -0.0133 0.1570 -0.2028 -0.0610 0.3444 -0.2550 -0.2520
0.2814 0.1484 -0.2160 -0.2955 -0.0916 -0.2219 0.1633 -0.0893 0.2445 -0.0321 0.1050 -0.0605 -0.1652 0.2124 0.1500
-0.1577 -0.1043 -0.2068 0.1893 -0.0222 -0.0729 -0.1530 -0.1266 -0.2775 0.1618 0.0577 -0.1290 0.2510 -0.1284 0.0115
0.1031 0.0449 -0.1046 -0.1297 0.0032 -0.0581 0.4791 -0.1709 -0.0713 -0.3076 0.2234 0.1020 -0.1763 -0.2292 -0.1775
-0.0168 -0.4317 0.5284 -0.1263 -0.1363 0.1733 -0.0351 0.4969 -0.1156 0.2594 0.3123 0.0831 0.3047 -0.2541 -0.2271
0.0721 -0.0374 0.3405 0.0829 -0.1702 0.2369 -0.0877 0.2061 0.0129 0.2107 -0.0643 0.0257 0.0772 0.0247 0.0357
0.0247 0.2717 -0.1427 0.1146 0.0059 0.1535 0.1246 0.2003 -0.2250 0.1243 -0.1998 0.1124 -0.3166 0.0387 -0.0584
0.0337 -0.0728 -0.7204 0.1083 0.3385 -0.0489 0.0150 -0.3123 -0.2450 0.3395 -0.0266 -0.1464 -0.4650 0.0045 0.0010
0.0821 -0.1057 -0.0466 0.0600 0.2162 0.0491 0.2277 -0.2366 0.5264 0.0260 0.1533 -0.1545 -0.1691 0.1263 0.4739
0.5618 -0.1389 0.1311 -0.2366 -0.2612 0.1054 0.4214 -0.0968 0.2378 0.0742 0.1362 -0.0443 0.1859 -0.1439 -0.0511
-0.1389 0.5639 -0.2361 0.1229 0.0935 -0.0847 -0.0760 0.0770 -0.0814 -0.3276 -0.2688 0.1306 -0.2093 0.1579 0.0904
0.1311 -0.2361 0.8412 -0.3004 -0.2777 0.1211 -0.2301 0.3626 0.1841 -0.0530 0.1751 0.1391 0.3622 -0.0307 -0.2654
-0.2366 0.1229 -0.3004 0.7172 0.3009 0.4748 0.2397 -0.0594 -0.2792 0.3463 -0.3037 -0.0301 -0.3640 -0.1323 0.2454
-0.2612 0.0935 -0.2777 0.3009 0.5920 0.0779 -0.0899 -0.2107 0.1831 -0.1457 -0.0197 -0.0219 -0.4566 0.0721 0.3146
0.1054 -0.0847 0.1211 0.4748 0.0779 0.6433 0.4315 0.0993 -0.2028 0.4349 -0.1294 0.0469 -0.1881 -0.2934 -0.0284
0.4214 -0.0760 -0.2301 0.2397 -0.0899 0.4315 1.1878 -0.0961 -0.0734 0.2712 -0.1223 -0.0191 0.1098 -0.4717 -0.1285
-0.0968 0.0770 0.3626 -0.0594 -0.2107 0.0993 -0.0961 0.7490 -0.2378 0.1050 -0.0866 0.1984 0.3329 -0.0800 -0.2338
0.2378 -0.0814 0.1841 -0.2792 0.1831 -0.2028 -0.0734 -0.2378 1.1624 -0.3832 -0.0479 -0.1837 0.2305 0.3955 0.6359
0.0742 -0.3276 -0.0530 0.3463 -0.1457 0.4349 0.2712 0.1050 -0.3832 0.9227 -0.0834 -0.1482 -0.0324 -0.1425 -0.0706
0.1362 -0.2688 0.1751 -0.3037 -0.0197 -0.1294 -0.1223 -0.0866 -0.0479 -0.0834 0.6686 0.0149 -0.0792 -0.1867 -0.1316
-0.0443 0.1306 0.1391 -0.0301 -0.0219 0.0469 -0.0191 0.1984 -0.1837 -0.1482 0.0149 0.1645 -0.0320 -0.0962 -0.2218
0.1859 -0.2093 0.3622 -0.3640 -0.4566 -0.1881 0.1098 0.3329 0.2305 -0.0324 -0.0792 -0.0320 0.9048 -0.0456 -0.1577
-0.1439 0.1579 -0.0307 -0.1323 0.0721 -0.2934 -0.4717 -0.0800 0.3955 -0.1425 -0.1867 -0.0962 -0.0456 0.4988 0.3098
-0.0511 0.0904 -0.2654 0.2454 0.3146 -0.0284 -0.1285 -0.2338 0.6359 -0.0706 -0.1316 -0.2218 -0.1577 0.3098 0.8820
-0.0817 -0.0692 0.4338 -0.1519 -0.0458 0.0668 -0.2008 0.2467 0.0650 0.1479 -0.1583 0.0835 0.0988 0.2058 -0.3573
0.1416 0.0187 0.0680 -0.0562 -0.0146 -0.0171 -0.2208 -0.0073 0.2197 -0.1295 0.0158 -0.0208 0.0281 0.0550 0.2491
-0.4990 0.0574 -0.2256 0.2206 0.3842 -0.1397 -0.4567 -0.0353 -0.0290 -0.1550 -0.2281 -0.0193 -0.1279 0.1608 0.1671
-0.1164 -0.1255 -0.0674 -0.2109 -0.0759 -0.4299 -0.3661 -0.1675 0.3490 -0.1026 0.1468 -0.2091 0.2150 0.2954 0.3980
0.0063 -0.2136 0.4850 -0.2805 -0.3146 -0.0851 -0.2834 0.4075 -0.1096 -0.0682 0.1309 0.1055 0.5218 -0.1392 -0.2866
Columns 46 through 50
-0.1545 0.0411 -0.2357 0.2349 0.0271
-0.2713 -0.0787 0.3986 -0.1041 -0.3102
-0.3821 0.3134 -0.1687 0.1657 -0.0949
0.1368 -0.0046 -0.2010 0.1751 0.2027
0.0131 -0.1013 -0.3998 -0.1983 0.0852
-0.0251 0.3395 0.2651 0.3247 0.4680
0.0240 -0.1217 0.2607 0.2915 0.0069
-0.0059 0.0351 0.2268 0.0951 0.2586
0.0621 -0.1643 0.1089 -0.3150 -0.5133
-0.1232 0.2941 0.1718 0.0387 0.6809
-0.1039 -0.3255 -0.1816 -0.3997 0.0700
-0.0018 0.2577 0.0235 0.4270 0.0981
-0.1081 0.0585 -0.1149 0.2562 0.2209
0.1816 -0.0722 -0.2578 0.0518 -0.0654
0.2430 -0.1540 0.2615 -0.2561 0.0935
0.0275 -0.1500 0.5355 -0.0885 0.3065
0.2651 -0.1471 -0.1411 0.2229 0.1538
0.2166 -0.0139 0.0452 -0.0299 0.0976
-0.2113 0.0765 0.1215 -0.0124 -0.2684
0.2603 0.1940 0.1902 -0.1432 -0.0850
0.2496 0.0436 -0.4046 0.0235 0.2965
0.1726 -0.3935 0.0411 -0.0815 -0.0311
-0.0389 -0.0344 -0.4058 0.1245 -0.3603
-0.2758 0.0330 0.2525 0.2248 0.1956
-0.1997 -0.2192 -0.2668 -0.0721 -0.2121
0.1897 -0.0419 -0.0716 -0.0147 0.4772
0.1525 0.0964 -0.1605 -0.0504 0.1421
0.0904 -0.0170 -0.1132 -0.3070 -0.2157
-0.1338 0.0823 0.3145 -0.2132 -0.3283
-0.0667 -0.1049 -0.2036 0.2714 -0.3742
-0.0817 0.1416 -0.4990 -0.1164 0.0063
-0.0692 0.0187 0.0574 -0.1255 -0.2136
0.4338 0.0680 -0.2256 -0.0674 0.4850
-0.1519 -0.0562 0.2206 -0.2109 -0.2805
-0.0458 -0.0146 0.3842 -0.0759 -0.3146
0.0668 -0.0171 -0.1397 -0.4299 -0.0851
-0.2008 -0.2208 -0.4567 -0.3661 -0.2834
0.2467 -0.0073 -0.0353 -0.1675 0.4075
0.0650 0.2197 -0.0290 0.3490 -0.1096
0.1479 -0.1295 -0.1550 -0.1026 -0.0682
-0.1583 0.0158 -0.2281 0.1468 0.1309
0.0835 -0.0208 -0.0193 -0.2091 0.1055
0.0988 0.0281 -0.1279 0.2150 0.5218
0.2058 0.0550 0.1608 0.2954 -0.1392
-0.3573 0.2491 0.1671 0.3980 -0.2866
0.7949 -0.2198 -0.0021 -0.2056 0.0743
-0.2198 0.3682 0.0427 0.0429 0.1559
-0.0021 0.0427 0.7153 0.0822 0.0522
-0.2056 0.0429 0.0822 0.6820 0.0415
0.0743 0.1559 0.0522 0.0415 0.6538
编辑
这就是我试图解决我的问题的方式：
Sig = cov(x); % Covariance calculation of a 12x50 matrix
A = chol(Sig);
Sigma = A*A';
p = mvnrnd(mu,Sigma,20);
但是当我运行它时，它给了我这个错误：
Matrix must be positive definite.
Error at A = chol(Sig);
我的矩阵仍然不是正定的。知道如何解决这个问题吗？