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