Data Fundamentals (H) - Week 10 Quiz
1. The Nyquist limit \(f_n\) is equal to:
1.0Hz
half the amplitude quantization levels
twice the sampling rate \(f_s\)
half the sampling rate \(f_s\)
twice the amplitude quantization levels
2. Decreasing the number of levels of amplitude quantization will have what affect on the sampled representation of a signal?
Increased SNR
Decreased Nyquist rate
Frequency shift
Decreased SNR
No effect
3. The exponential smooth is often used instead of a moving average because:
it is more numerically stable
it is probabilistic
it requires storing/computing less data
it is super-quadratic
it is nonlinear
4. Aliasing is caused by sampling signals with:
noise levels less than the maximum SNR
frequencies greater than the Nyquist limit
undefined values present
frequencies less than the Nyquist limit
noise levels greater than the maximum SNR
5. Along with a way to evaluate the likelihood and prior at any parameter setting \(\theta\), what else does Metropolis-Hastings need to sample from the posterior distribution?
A maximum likelihood estimation procedure.
A proposal distribution \(q(\theta^\prime|\theta)\)
A way to evaluate the evidence \(P(\theta)[/theta]
An integration function \(V(\theta|D)\)
The square root of 2.
6. In medical device, if you had an initial heart/pulse rate
p0
and a 1D vector of changes in pulse rates captured at evenly spaced intervals,
delta_p
, how would you compute
p
, the pulse rate at each of these times?
p = np.sum(delta_p) + p0
p = p0 * delta_p
p = p0 + delta_p[:]
p = p0 + np.cumsum(delta_p)
p = np.prod(delta_p) * p0
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