Solving the $H_{0}$ tension in $f(T)$ Gravity through Bayesian Machine
  Learning
  Bayesian Machine Learning(BML) and strong lensing time delay(SLTD) techniques
are used in order to tackle the $H_{0}$ tension in $f(T)$ gravity. The power of
BML relies on employing a model-based generative process which already plays an
important role in different domains of cosmology and astrophysics, being the
present work a further proof of this. Three viable $f(T)$ models are
considered: a power law, an exponential, and a squared exponential model. The
learned constraints and respective results indicate that the exponential model,
$f(T)=\alpha T_{0}\left(1-e^{-pT/T_{0}}\right)$, has the capability to solve
the $H_{0}$ tension quite efficiently. The forecasting power and robustness of
the method are shown by considering different redshift ranges and parameters
for the lenses and sources involved. The lesson learned is that these values
can strongly affect our understanding of the $H_{0}$ tension, as it does happen
in the case of the model considered. The resulting constraints of the learning
method are eventually validated by using the observational Hubble data(OHD).
