{"id":5685,"date":"2025-12-07T23:03:49","date_gmt":"2025-12-07T19:33:49","guid":{"rendered":"https:\/\/aidreamslab.com\/blog\/%d8%b1%d8%a7%d9%87%d9%86%d9%85%d8%a7%db%8c-%da%a9%d8%a7%d9%85%d9%84-%d9%be%db%8c%d8%a7%d8%af%d9%87%d8%b3%d8%a7%d8%b2%db%8c-%d8%aa%d8%ad%d9%84%db%8c%d9%84-%d8%b1%da%af%d8%b1%d8%b3%db%8c%d9%88\/"},"modified":"2025-12-07T23:04:57","modified_gmt":"2025-12-07T19:34:57","slug":"%d8%b1%d8%a7%d9%87%d9%86%d9%85%d8%a7%db%8c-%da%a9%d8%a7%d9%85%d9%84-%d9%be%db%8c%d8%a7%d8%af%d9%87%d8%b3%d8%a7%d8%b2%db%8c-%d8%aa%d8%ad%d9%84%db%8c%d9%84-%d8%b1%da%af%d8%b1%d8%b3%db%8c%d9%88","status":"publish","type":"post","link":"https:\/\/aidreamslab.com\/blog\/%d8%b1%d8%a7%d9%87%d9%86%d9%85%d8%a7%db%8c-%da%a9%d8%a7%d9%85%d9%84-%d9%be%db%8c%d8%a7%d8%af%d9%87%d8%b3%d8%a7%d8%b2%db%8c-%d8%aa%d8%ad%d9%84%db%8c%d9%84-%d8%b1%da%af%d8%b1%d8%b3%db%8c%d9%88\/","title":{"rendered":"\u0631\u0627\u0647\u0646\u0645\u0627\u06cc \u06a9\u0627\u0645\u0644 \u067e\u06cc\u0627\u062f\u0647\u200c\u0633\u0627\u0632\u06cc \u062a\u062d\u0644\u06cc\u0644 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0628\u06cc\u0632\u06cc \u0633\u0644\u0633\u0644\u0647 \u0645\u0631\u0627\u062a\u0628\u06cc \u0628\u0627 NumPyro"},"content":{"rendered":"

\u0645\u0642\u062f\u0645\u0647<\/h2>\n

\u062a\u062d\u0644\u06cc\u0644 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0628\u06cc\u0632\u06cc \u06cc\u06a9\u06cc \u0627\u0632 \u062a\u06a9\u0646\u06cc\u06a9\u200c\u0647\u0627\u06cc \u0642\u062f\u0631\u062a\u0645\u0646\u062f \u062f\u0631 \u0622\u0645\u0627\u0631 \u0648 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u0627\u0633\u062a \u06a9\u0647 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u062f \u0628\u0647 \u0645\u0627 \u062f\u0631 \u062f\u0631\u06a9 \u0627\u0644\u06af\u0648\u0647\u0627 \u0648 \u0631\u0648\u0627\u0628\u0637 \u062f\u0631 \u062f\u0627\u062f\u0647\u200c\u0647\u0627 \u06a9\u0645\u06a9 \u06a9\u0646\u062f. \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647\u060c \u0645\u0627 \u0628\u0647 \u0628\u0631\u0631\u0633\u06cc \u067e\u06cc\u0627\u062f\u0647\u200c\u0633\u0627\u0632\u06cc \u0639\u0645\u0644\u06cc \u06cc\u06a9 \u062a\u062d\u0644\u06cc\u0644 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0628\u06cc\u0632\u06cc \u0633\u0644\u0633\u0644\u0647 \u0645\u0631\u0627\u062a\u0628\u06cc \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 NumPyro<\/strong> \u062e\u0648\u0627\u0647\u06cc\u0645 \u067e\u0631\u062f\u0627\u062e\u062a. NumPyro<\/strong> \u06cc\u06a9 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0645\u062f\u0631\u0646 \u0628\u0631\u0627\u06cc \u0627\u0633\u062a\u0646\u0628\u0627\u0637 \u0628\u06cc\u0632\u06cc \u062f\u0631 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0627\u0633\u062a \u06a9\u0647 \u0628\u0631 \u0627\u0633\u0627\u0633 JAX<\/strong> \u0633\u0627\u062e\u062a\u0647 \u0634\u062f\u0647 \u0648 \u0628\u0647 \u0645\u0627 \u0627\u0645\u06a9\u0627\u0646 \u0645\u06cc\u200c\u062f\u0647\u062f \u062a\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0633\u0631\u0639\u062a \u0648 \u06a9\u0627\u0631\u0627\u06cc\u06cc \u0628\u0627\u0644\u0627\u062a\u0631\u060c \u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u067e\u06cc\u0686\u06cc\u062f\u0647\u200c\u0627\u06cc \u0631\u0627 \u062a\u0648\u0633\u0639\u0647 \u062f\u0647\u06cc\u0645.<\/p>\n

\u062a\u0648\u0644\u06cc\u062f \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u0645\u0635\u0646\u0648\u0639\u06cc<\/h2>\n

\u0642\u0628\u0644 \u0627\u0632 \u0622\u0646\u06a9\u0647 \u0628\u0647 \u062a\u062d\u0644\u06cc\u0644 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0628\u067e\u0631\u062f\u0627\u0632\u06cc\u0645\u060c \u0627\u0628\u062a\u062f\u0627 \u0644\u0627\u0632\u0645 \u0627\u0633\u062a \u06a9\u0647 \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u0645\u0635\u0646\u0648\u0639\u06cc \u062a\u0648\u0644\u06cc\u062f \u06a9\u0646\u06cc\u0645. \u0627\u06cc\u0646 \u062f\u0627\u062f\u0647\u200c\u0647\u0627 \u0628\u0647 \u0645\u0627 \u06a9\u0645\u06a9 \u0645\u06cc\u200c\u06a9\u0646\u0646\u062f \u062a\u0627 \u0628\u062a\u0648\u0627\u0646\u06cc\u0645 \u0645\u062f\u0644 \u062e\u0648\u062f \u0631\u0627 \u0622\u0632\u0645\u0627\u06cc\u0634 \u06a9\u0631\u062f\u0647 \u0648 \u0631\u0641\u062a\u0627\u0631\u0634 \u0631\u0627 \u062f\u0631 \u0634\u0631\u0627\u06cc\u0637 \u0645\u062e\u062a\u0644\u0641 \u0628\u0631\u0631\u0633\u06cc \u06a9\u0646\u06cc\u0645. \u0645\u0627 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u0627\u0632 \u062a\u0627\u0628\u0639 numpy.random.normal<\/code> \u0628\u0631\u0627\u06cc \u062a\u0648\u0644\u06cc\u062f \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u062a\u0635\u0627\u062f\u0641\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u0645.<\/p>\n

\u0645\u062b\u0627\u0644 \u06a9\u062f \u062a\u0648\u0644\u06cc\u062f \u062f\u0627\u062f\u0647<\/h3>\n
import numpy as np\n\n# \u062a\u0646\u0638\u06cc\u0645 \u062a\u0635\u0627\u062f\u0641\u06cc \u0628\u0630\u0631 \u0628\u0631\u0627\u06cc \u062a\u0648\u0644\u06cc\u062f \u062f\u0627\u062f\u0647\nnp.random.seed(42)\n\n# \u062a\u0639\u062f\u0627\u062f \u0645\u0634\u0627\u0647\u062f\u0627\u062a\nn = 100\n# \u062a\u0648\u0644\u06cc\u062f \u062f\u0627\u062f\u0647\u200c\u0647\u0627\nX = np.random.normal(size=n)\ntrue_weights = 2.0\ntrue_bias = 5.0\nY = true_weights * X + true_bias + np.random.normal(scale=1.0, size=n)<\/code><\/pre>\n
\u062a\u0639\u0631\u06cc\u0641 \u0645\u062f\u0644 \u0627\u062d\u062a\u0645\u0627\u0644\u06cc<\/h2>\n
\u062d\u0627\u0644\u0627 \u06a9\u0647 \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u062e\u0648\u062f \u0631\u0627 \u062f\u0627\u0631\u06cc\u0645\u060c \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u0627\u0642\u062f\u0627\u0645 \u0628\u0647 \u062a\u0639\u0631\u06cc\u0641 \u0645\u062f\u0644 \u0627\u062d\u062a\u0645\u0627\u0644\u06cc \u0628\u0631\u0627\u06cc \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0628\u06cc\u0632\u06cc \u06a9\u0646\u06cc\u0645. \u0645\u062f\u0644 \u0645\u0627 \u0628\u0647 \u062f\u0646\u0628\u0627\u0644 \u0634\u0646\u0627\u0633\u0627\u06cc\u06cc \u0648\u0632\u0646\u200c\u0647\u0627 \u0648 \u0628\u0627\u06cc\u0627\u0633 \u0648\u0627\u0642\u0639\u06cc \u0627\u0632 \u062f\u0627\u062f\u0647\u200c\u0647\u0627 \u0627\u0633\u062a. \u062f\u0631 NumPyro<\/strong>\u060c \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u0627\u0632 \u0633\u0627\u062e\u062a\u0627\u0631\u0647\u0627\u06cc numpyro.sample<\/code> \u0628\u0631\u0627\u06cc \u0645\u0634\u062e\u0635 \u06a9\u0631\u062f\u0646 \u062a\u0648\u0632\u06cc\u0639\u200c\u0647\u0627\u06cc \u067e\u06cc\u0634\u06cc\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u0645.<\/p>\n
\u0645\u062f\u0644 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0628\u06cc\u0632\u06cc<\/h3>\n
import numpyro\nfrom numpyro import distributions as dist\n\n# \u062a\u0639\u0631\u06cc\u0641 \u0645\u062f\u0644\n def model(X, Y):\n    # \u062a\u0648\u0632\u06cc\u0639 \u067e\u06cc\u0634\u06cc\u0646 \u0628\u0631\u0627\u06cc \u0648\u0632\u0646\n    weight = numpyro.sample('weight', dist.Normal(0, 1))\n    # \u062a\u0648\u0632\u06cc\u0639 \u067e\u06cc\u0634\u06cc\u0646 \u0628\u0631\u0627\u06cc \u0628\u0627\u06cc\u0627\u0633\n    bias = numpyro.sample('bias', dist.Normal(0, 1))\n    # \u0645\u062f\u0644 \u0627\u062d\u062a\u0645\u0627\u0644 \u0628\u0631\u0627\u06cc \u062f\u0627\u062f\u0647\u200c\u0647\u0627\n    with numpyro.plate('data', X.shape[0]):\n        numpyro.sample('obs', dist.Normal(weight * X + bias, 1), obs=Y)<\/code><\/pre>\n
\u0627\u0633\u062a\u0646\u0628\u0627\u0637 \u0628\u0627 NUTS<\/h2>\n
\u0628\u0631\u0627\u06cc \u0627\u0633\u062a\u0646\u0628\u0627\u0637 \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u0645\u062f\u0644\u060c \u0627\u0632 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 NUTS (No-U-Turn Sampler) \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f \u06a9\u0647 \u06cc\u06a9\u06cc \u0627\u0632 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0631\u0648\u0634\u200c\u0647\u0627 \u0628\u0631\u0627\u06cc \u0627\u0633\u062a\u0646\u0628\u0627\u0637 \u062f\u0631 \u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u0628\u06cc\u0632\u06cc \u067e\u06cc\u0686\u06cc\u062f\u0647 \u0627\u0633\u062a. \u0645\u0627 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u0627\u0632 \u062a\u0627\u0628\u0639 numpyro.infer.NUTS<\/code> \u0628\u0631\u0627\u06cc \u067e\u06cc\u0627\u062f\u0647\u200c\u0633\u0627\u0632\u06cc \u0627\u0633\u062a\u0646\u0628\u0627\u0637 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u0645.<\/p>\n
\u0622\u063a\u0627\u0632 \u0627\u0633\u062a\u0646\u0628\u0627\u0637<\/h3>\n
from numpyro.infer import MCMC, NUTS\n\n# \u062a\u0646\u0638\u06cc\u0645\u0627\u062a\u06cc \u0628\u0631\u0627\u06cc \u0627\u0633\u062a\u0646\u0628\u0627\u0637\nnuts_kernel = NUTS(model)\n\n# MCMC \u0628\u0627 1000 \u06af\u0627\u0645 \u0646\u0645\u0648\u0646\u0647\u200c\u06af\u06cc\u0631\u06cc\nmcmc = MCMC(nuts_kernel, num_warmup=500, num_samples=1000)\nmcmc.run(X, Y)<\/code><\/pre>\n
\u062a\u062d\u0644\u06cc\u0644 \u062a\u0648\u0632\u06cc\u0639\u200c\u0647\u0627\u06cc \u067e\u0633\u06cc\u0646<\/h2>\n
\u067e\u0633 \u0627\u0632 \u0627\u0633\u062a\u0646\u0628\u0627\u0637\u060c \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u062a\u0648\u0632\u06cc\u0639\u200c\u0647\u0627\u06cc \u067e\u0633\u06cc\u0646 \u0648\u0632\u0646 \u0648 \u0628\u0627\u06cc\u0627\u0633 \u0631\u0627 \u0628\u0631\u0631\u0633\u06cc \u06a9\u0646\u06cc\u0645. \u0628\u0631\u0627\u06cc \u062a\u062d\u0644\u06cc\u0644 \u0628\u06cc\u0634\u062a\u0631\u060c \u0645\u0627 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u0645\u0642\u0627\u062f\u06cc\u0631 \u0645\u06cc\u0627\u0646\u06af\u06cc\u0646\u060c \u0648\u0627\u0631\u06cc\u0627\u0646\u0633\u060c \u0648 \u0633\u0627\u06cc\u0631 \u0645\u062a\u0631\u0627\u062f\u0641\u200c\u0647\u0627\u06cc \u0622\u0645\u0627\u0631\u06cc \u0631\u0627 \u0627\u0633\u062a\u062e\u0631\u0627\u062c \u06a9\u0646\u06cc\u0645.<\/p>\n
\u062a\u062d\u0644\u06cc\u0644 \u0646\u062a\u0627\u06cc\u062c<\/h3>\n
samples = mcmc.get_samples()\nprint(\"Weight mean:\", samples['weight'].mean())\nprint(\"Bias mean:\", samples['bias'].mean())<\/code><\/pre>\n
\u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc\u200c\u0647\u0627\u06cc \u067e\u0633\u06cc\u0646<\/h2>\n
\u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u0648\u0632\u06cc\u0639 \u067e\u0633\u06cc\u0646 \u0645\u062f\u0644\u060c \u0645\u0627 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc\u200c\u0647\u0627\u06cc \u062c\u062f\u06cc\u062f\u06cc \u0627\u0646\u062c\u0627\u0645 \u062f\u0647\u06cc\u0645. \u0627\u06cc\u0646 \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc\u200c\u0647\u0627 \u0628\u0647 \u0645\u0627 \u06a9\u0645\u06a9 \u0645\u06cc\u200c\u06a9\u0646\u0646\u062f \u062a\u0627 \u062f\u0631\u06a9 \u0628\u0647\u062a\u0631\u06cc \u0627\u0632 \u0631\u0641\u062a\u0627\u0631 \u0645\u062f\u0644 \u062f\u0631 \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u062c\u062f\u06cc\u062f \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u0645. \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc\u200c\u0647\u0627\u06cc \u062c\u062f\u06cc\u062f \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u0646\u062f \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u0648\u0632\u06cc\u0639\u200c\u0647\u0627\u06cc \u06cc\u0627\u062f\u0634\u062f\u0647 \u0627\u0646\u062c\u0627\u0645 \u0634\u0648\u0646\u062f.<\/p>\n
\u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc<\/h3>\n
# \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u0648\u0632\u06cc\u0639\u200c\u0647\u0627\u06cc \u067e\u0633\u06cc\u0646\npredicted_y = samples['weight'].mean() * X_new + samples['bias'].mean()<\/code><\/pre>\n
\u062c\u0645\u0639\u200c\u0628\u0646\u062f\u06cc \u0648 \u0646\u062a\u06cc\u062c\u0647\u200c\u06af\u06cc\u0631\u06cc<\/h2>\n
\u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647\u060c \u0645\u0627 \u0628\u0647 \u0637\u0648\u0631 \u062c\u0627\u0645\u0639 \u0628\u0647 \u067e\u06cc\u0627\u062f\u0647\u200c\u0633\u0627\u0632\u06cc \u06cc\u06a9 \u0645\u062f\u0644 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0628\u06cc\u0632\u06cc \u0633\u0644\u0633\u0644\u0647 \u0645\u0631\u0627\u062a\u0628\u06cc \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 NumPyro<\/strong> \u067e\u0631\u062f\u0627\u062e\u062a\u06cc\u0645. \u0627\u0632 \u062a\u0648\u0644\u06cc\u062f \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u0645\u0635\u0646\u0648\u0639\u06cc \u06af\u0631\u0641\u062a\u0647 \u062a\u0627 \u062a\u0639\u0631\u06cc\u0641 \u0645\u062f\u0644\u060c \u0627\u0633\u062a\u0646\u0628\u0627\u0637\u060c \u062a\u062d\u0644\u06cc\u0644 \u062a\u0648\u0632\u06cc\u0639\u200c\u0647\u0627\u06cc \u067e\u0633\u06cc\u0646 \u0648 \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc\u200c\u0647\u0627\u060c \u062a\u0645\u0627\u0645\u06cc \u0645\u0631\u0627\u062d\u0644 \u0628\u0647 \u062a\u0641\u0635\u06cc\u0644 \u062a\u0648\u0636\u06cc\u062d \u062f\u0627\u062f\u0647 \u0634\u062f\u0646\u062f. \u0628\u0627 \u06af\u0633\u062a\u0631\u0634 \u062a\u0648\u0627\u0646\u0627\u06cc\u06cc\u200c\u0647\u0627\u06cc \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc<\/strong> \u062f\u0631 \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u060c \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0631\u0648\u0634\u200c\u0647\u0627\u06cc \u0628\u0627\u06cc\u0632\u06cc \u0628\u0647 \u06cc\u06a9 \u0627\u0628\u0632\u0627\u0631 \u0636\u0631\u0648\u0631\u06cc \u0628\u0631\u0627\u06cc \u062a\u062d\u0644\u06cc\u0644\u06af\u0631\u0627\u0646 \u062a\u0628\u062f\u06cc\u0644 \u0634\u062f\u0647 \u0627\u0633\u062a. \u0627\u06cc\u0646 \u062a\u06a9\u0646\u06cc\u06a9\u200c\u0647\u0627 \u0628\u0647 \u0645\u0627 \u0627\u062c\u0627\u0632\u0647 \u0645\u06cc\u200c\u062f\u0647\u0646\u062f \u062a\u0627 \u0628\u0627 \u0627\u0637\u0645\u06cc\u0646\u0627\u0646 \u0628\u06cc\u0634\u062a\u0631\u06cc \u0646\u0633\u0628\u062a \u0628\u0647 \u0646\u062a\u0627\u06cc\u062c \u0628\u0647\u200c\u062f\u0633\u062a\u200c\u0622\u0645\u062f\u0647\u060c \u062a\u0635\u0645\u06cc\u0645\u200c\u06af\u06cc\u0631\u06cc \u06a9\u0646\u06cc\u0645.<\/p>\n","protected":false},"excerpt":{"rendered":"
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NumPyro \u06cc\u06a9 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0645\u062f\u0631\u0646 \u0628\u0631\u0627\u06cc \u0627\u0633\u062a\u0646\u0628\u0627\u0637 \u0628\u06cc\u0632\u06cc \u062f\u0631 […]<\/p>\n","protected":false},"author":6,"featured_media":5686,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_yoast_wpseo_focuskw":"\u062a\u062d\u0644\u06cc\u0644 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0628\u06cc\u0632\u06cc","_yoast_wpseo_title":"\u0631\u0627\u0647\u0646\u0645\u0627\u06cc \u06a9\u0627\u0645\u0644 \u067e\u06cc\u0627\u062f\u0647\u200c\u0633\u0627\u0632\u06cc \u062a\u062d\u0644\u06cc\u0644 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0628\u06cc\u0632\u06cc \u0633\u0644\u0633\u0644\u0647 \u0645\u0631\u0627\u062a\u0628\u06cc \u0628\u0627 NumPyro","_yoast_wpseo_metadesc":"\u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u062a\u062f\u0631\u06cc\u0633 \u062a\u062d\u0644\u06cc\u0644 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