Data Fundamentals (H) - Week 10 Quiz
1. The Nyquist limit \(f_n\) is equal to:
twice the amplitude quantization levels
half the sampling rate \(f_s\)
twice the sampling rate \(f_s\)
half the amplitude quantization levels
1.0Hz
2. Decreasing the number of levels of amplitude quantization will have what affect on the sampled representation of a signal?
Decreased Nyquist rate
Frequency shift
No effect
Increased SNR
Decreased SNR
3. The exponential smooth is often used instead of a moving average because:
it is nonlinear
it is probabilistic
it is more numerically stable
it is super-quadratic
it requires storing/computing less data
4. Aliasing is caused by sampling signals with:
noise levels greater than the maximum SNR
undefined values present
frequencies greater than the Nyquist limit
frequencies less than the Nyquist limit
noise levels less 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?
The square root of 2.
A maximum likelihood estimation procedure.
A proposal distribution \(q(\theta^\prime|\theta)\)
A way to evaluate the evidence \(P(\theta)[/theta]
An integration function \(V(\theta|D)\)
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 + np.cumsum(delta_p)
p = p0 + delta_p[:]
p = p0 * delta_p
p = np.prod(delta_p) * p0
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