Step 1: Understanding the formula for the bulk partition coefficient.
The bulk partition coefficient (\(K_d\)) for the rock is calculated as a weighted average based on the modal abundances and the partition coefficients of each mineral: \[ K_{{bulk}} = (f_1 \times K_{d1}) + (f_2 \times K_{d2}) + (f_3 \times K_{d3}) \] Where:
\(f_1, f_2, f_3\) are the modal abundances of M1, M2, and M3, respectively.
\(K_{d1}, K_{d2}, K_{d3}\) are the partition coefficients of element E in M1, M2, and M3, respectively.
Step 2: Applying the values.
Given:
\(K_{d1} = 1.5, K_{d2} = 1.0, K_{d3} = 0.5\)
\(f_1 = 10% = 0.1, f_2 = 40% = 0.4, f_3 = 50% = 0.5\)
The bulk partition coefficient is: \[ K_{{bulk}} = (0.1 \times 1.5) + (0.4 \times 1.0) + (0.5 \times 0.5) = 0.15 + 0.4 + 0.25 = 0.8 \] Thus, the bulk partition coefficient of element E in the rock is \(0.80\).
A color model is shown in the figure with color codes: Yellow (Y), Magenta (M), Cyan (Cy), Red (R), Blue (Bl), Green (G), and Black (K). Which one of the following options displays the color codes that are consistent with the color model?
While doing Bayesian inference, consider estimating the posterior distribution of the model parameter (m), given data (d). Assume that Prior and Likelihood are proportional to Gaussian functions given by \[ {Prior} \propto \exp(-0.5(m - 1)^2) \] \[ {Likelihood} \propto \exp(-0.5(m - 3)^2) \]
The mean of the posterior distribution is (Answer in integer)