We present a Bayesian method for estimating instrumental noise parameters and propagating noise uncertainties within the global BeyondPlanck Gibbs sampling framework, and apply this to Planck LFI time-ordered data. Following previous literature, we adopt a simple $1/f$ model for the noise power spectral density (PSD), and implement an optimal Wiener-filter (or constrained realization) gap-filling procedure to account for masked data. We then use this procedure to both estimate the gapless correlated noise in the time-domain, $n_mathrm{corr}$, and to sample the noise PSD spectral parameters, $xi^n = {sigma_0, f_mathrm{knee}, alpha}$. In contrast to previous Planck analyses, we only assume piecewise stationary noise within each pointing period (PID), not throughout the full mission, but we adopt the LFI DPC results as priors on $alpha$ and $f_mathrm{knee}$. On average, we find best-fit correlated noise parameters that are mostly consistent with previous results, with a few notable exceptions. However, a detailed inspection of the time-dependent results reveals many important findings. First and foremost, we find strong evidence for statistically significant temporal variations in all noise PSD parameters, many of which are directly correlated with satellite housekeeping data. Second, while the simple $1/f$ model appears to be an excellent fit for the LFI 70 GHz channel, there is evidence for additional correlated noise not described by a $1/f$ model in the 30 and 44 GHz channels, including within the primary science frequency range of 0.1-1 Hz. In general, most 30 and 44 GHz channels exhibit excess noise at the 2-$3 sigma$ level in each one hour pointing period. For some periods of time, we also find evidence of strong common mode noise fluctuations across the entire focal plane. (Abridged.)
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