Data Fundamentals (H) - Week 10 Quiz
1. The Nyquist limit \(f_n\) is equal to:
half the amplitude quantization levels
half the sampling rate \(f_s\)
1.0Hz
twice the amplitude quantization levels
twice the sampling rate \(f_s\)
2. Decreasing the number of levels of amplitude quantization will have what affect on the sampled representation of a signal?
Decreased Nyquist rate
Decreased SNR
No effect
Increased SNR
Frequency shift
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 less than the Nyquist limit
noise levels greater than the maximum SNR
undefined values present
frequencies greater than the Nyquist limit
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 \(q(\theta^\prime|\theta)\)
An integration function \(V(\theta|D)\)
A way to evaluate the evidence \(P(\theta)[/theta]
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 + np.cumsum(delta_p)
p = p0 * delta_p
p = np.prod(delta_p) * p0
p = p0 + delta_p[:]
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