Estimates of Parameters of GMM: The Expectation Maximization (EM) Algorithm We observe n data points x1,…,xn in Rd . We wish to maximize the GMM likelihood with respect to the parameter set θ={p1,…,pK,μ(1),…,μ(K),σ21,…,σ2K} . Maximizing the log-likelihood log(∏ni=1p(x(i)|θ)) is not tractable in the setting of GMMs. There is no closed-form solution to finding the parameter set θ that maximizes the likelihood. The EM algorithm is an iterative algorithm that finds a locally optimal solution θ^ to the GMM likelihood maximization problem.

The drive force for diffusion is 7 fick's first law can be used to solve the non-steady state diffusion. a)-true b)-false

Derive the correction factor formula for conical nozzle i=-(1+ cosa) and calculate the nozzle angle correction factor for a nozzle whose divergence hal-fangle is 13 (hint: assume that all the mass flow originates at the apex of the cone.

Tom is having a problem with his washing machine. he notices that the machine vibrates violently at a frequency of 1500 rpm due to an unknown rotating unbalance. the machine is mounted on 4 springs each having a stiffness of 10 kn/m. tom wishes to add an undamped vibration absorber attached by a spring under the machine the machine working frequency ranges between 800 rpm to 2000 rpm and its total mass while loaded is assumed to be 80 kg a) what should be the mass of the absorber added to the machine so that the natural frequency falls outside the working range? b) after a first trial of an absorber using a mass of 35 kg, the amplitude of the oscillation was found to be 10 cm. what is the value of the rotating unbalance? c) using me-3.5 kg.m, find the optimal absorber (by minimizing its mass). what would be the amplitude of the oscillation of the absorber?