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