<transcript><audio_10> अब Jacobian, Jacobian हमने कैसे निकालने है, तो Jacobian further इसका answer हमारे पास आ जाएगा, जब हम इसे apply करें, इसका Jacobian find करने है, तो इसका result आ जाएगा,</transcript><transcript><audio_11> 1 by 2. Yani variance covariance determinant of variance covariance matrix power minus 1 by 2. So, this is the x minus mu</transcript><transcript><audio_12> x-μ transpose σ x-μ किसके equal हो गया? z transpose σ inverse, z is σ-1, σ-1 by 2z कि जब हम values उसमें enter कर रहे हैं, ये सारी values, basically यहां</transcript> this is the transformation. Now, if we have to find out the Cobian, then the answer will come to us. When we apply it, we have to find the Cobian, then the result is coming. Sigma.  - 1/2 means the variance is the power of the variance determinant of the covariate matrix - 1/2 so this is the x - you got this term okay Inverse g e sigma minus sigma - 1/2 that when we enter the values ​​in it, basically how are all these values ​​coming from here,
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