diff --git a/ravenframework/Distributions.py b/ravenframework/Distributions.py
index eb27536cff..08066c68c1 100644
--- a/ravenframework/Distributions.py
+++ b/ravenframework/Distributions.py
@@ -106,8 +106,20 @@ class cls.
         specifying input of cls.
     """
     inputSpecification = super().getInputSpecification()
-    inputSpecification.addSub(InputData.parameterInputFactory('upperBound', contentType=InputTypes.FloatType))
-    inputSpecification.addSub(InputData.parameterInputFactory('lowerBound', contentType=InputTypes.FloatType))
+    inputSpecification.addSub(
+      InputData.parameterInputFactory(
+        'upperBound',
+        descr=r"""the maximum value allowable by this distribution. For distributions that do not traditionally
+          include an upper bound, including this node will truncate (and rebalance the probability) of the distribution,
+          with this value as the maximum value.""",
+        contentType=InputTypes.FloatType))
+    inputSpecification.addSub(
+      InputData.parameterInputFactory(
+        'lowerBound',
+        descr=r"""the minimum value allowable by this distribution. For distributions that do not traditionally
+          include a lower bound, including this node will truncate (and rebalance the probability) of the distribution,
+          with this value as the minimum value.""",
+        contentType=InputTypes.FloatType))
     return inputSpecification
 
   def __init__(self):
@@ -514,6 +526,22 @@ class Uniform(BoostDistribution):
     Uniform univariate distribution
   """
 
+  @classmethod
+  def getInputSpecification(cls):
+    """
+      Method to get a reference to a class that specifies the input data for
+      class cls.
+      @ In, cls, the class for which we are retrieving the specification
+      @ Out, inputSpecification, InputData.ParameterInput, class to use for
+        specifying input of cls.
+    """
+    inputSpecification = super(Uniform, cls).getInputSpecification()
+    inputSpecification.description = r"""Classical uniform distribution. The probability density function for the uniform
+      distribution is given by $f(x)=\frac{1}{b-a}$ for $a \le x \le b$.
+      See \url{https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.uniform.html} for more details.
+      """
+    return inputSpecification
+
   def __init__(self, lowerBound=None, upperBound=None):
     """
       Constructor
@@ -636,8 +664,18 @@ class cls.
         specifying input of cls.
     """
     inputSpecification = super(Normal, cls).getInputSpecification()
-    inputSpecification.addSub(InputData.parameterInputFactory("mean", contentType=InputTypes.FloatType))
-    inputSpecification.addSub(InputData.parameterInputFactory("sigma", contentType=InputTypes.FloatType))
+    inputSpecification.description = r"""Classical Gaussian normal distribution. The probability density function for the normal
+      distribution is given by $f(x)=\frac{1}{\sqrt{2\pi\sigma^2}}e^{-\frac{(x-\mu)^2}{2\sigma^2}}$ for $-\infty\leq x \leq \infty$.
+      See \url{https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.norm.html} for more details.
+      """
+    inputSpecification.addSub(InputData.parameterInputFactory("mean",
+        descr=r"""the distribution mean or expected value. For a standard normal distribution,
+          this is zero. """,
+        contentType=InputTypes.FloatType))
+    inputSpecification.addSub(InputData.parameterInputFactory("sigma",
+        descr=r"""the standard deviation (stdv) of this distribution. For a standard normal distribution,
+          this is one. The variance of this distribution is the square of this value, $\sigma^2$.""",
+        contentType=InputTypes.FloatType))
 
     return inputSpecification
 
@@ -786,9 +824,24 @@ class cls.
         specifying input of cls.
     """
     inputSpecification = super(Gamma, cls).getInputSpecification()
-    inputSpecification.addSub(InputData.parameterInputFactory("low", contentType=InputTypes.FloatType))
-    inputSpecification.addSub(InputData.parameterInputFactory("alpha", contentType=InputTypes.FloatType))
-    inputSpecification.addSub(InputData.parameterInputFactory("beta", contentType=InputTypes.FloatType))
+    inputSpecification.description = r"""Classical gamma distribution. The probability density function for the gamma
+      distribution is given by $f(x,\alpha)=\frac{\beta^{\alpha}x^{\alpha-1}e^{-\beta x}}{\Gamma(\alpha)}$
+      for $x\geq 0,\alpha>0$, and where $\Gamma(\alpha)$ refers to the gamma function.
+      See \url{https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gamma.html} for more details.
+      """
+    inputSpecification.addSub(InputData.parameterInputFactory("low",
+        descr=r"""the lower domain value of this distribution. Setting this to a nonzero value will shift the
+          distribution to the left or right. \default{0.0} """,
+        contentType=InputTypes.FloatType))
+    inputSpecification.addSub(InputData.parameterInputFactory("alpha",
+        descr=r"""first shape parameter.
+          See \url{https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gamma.html} for more details.  """,
+        contentType=InputTypes.FloatType))
+    inputSpecification.addSub(InputData.parameterInputFactory("beta",
+        descr=r"""rate parameter, also inverse scale parameter.
+          See \url{https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gamma.html} for more details. \default{1.0}
+          """,
+        contentType=InputTypes.FloatType))
 
     return inputSpecification
 
@@ -942,11 +995,34 @@ class cls.
         specifying input of cls.
     """
     inputSpecification = super(Beta, cls).getInputSpecification()
-    inputSpecification.addSub(InputData.parameterInputFactory("low", contentType=InputTypes.FloatType))
-    inputSpecification.addSub(InputData.parameterInputFactory("alpha", contentType=InputTypes.FloatType))
-    inputSpecification.addSub(InputData.parameterInputFactory("beta", contentType=InputTypes.FloatType))
-    inputSpecification.addSub(InputData.parameterInputFactory("high", contentType=InputTypes.FloatType))
-    inputSpecification.addSub(InputData.parameterInputFactory("peakFactor", contentType=InputTypes.FloatType))
+    inputSpecification.description = r"""Classical beta distribution. The probability density function for the beta
+      distribution is given by $f(x;\alpha,\beta)=\frac{1}{B(\alpha,\beta)}x^{\alpha-1}(1-x)^{\beta-1}$
+      for $\alpha>0, \beta>0$ and $0 \leq x \leq 1$
+      and $B(\alpha,\beta)=\frac{\Gamma(\alpha)\Gamma(\beta)}{\Gamma(\alpha+\beta)}$.
+      See \url{https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.beta.html} for more details.
+      """
+    inputSpecification.addSub(InputData.parameterInputFactory("low",
+        descr=r"""the lower domain value of this distribution. Setting this to a nonzero value will shift the
+          distribution to the left or right. \default{0.0} """,
+        contentType=InputTypes.FloatType))
+    inputSpecification.addSub(InputData.parameterInputFactory("high",
+        descr=r"""the upper domain value of this distribution. Setting this to a nonzero value will shift the right side
+          of the distribution to the left or right. \default{0.0} """,
+        contentType=InputTypes.FloatType))
+    inputSpecification.addSub(InputData.parameterInputFactory("alpha",
+        descr=r"""first shape parameter. If specified, \xmlNode{beta} must also be provided, and
+          \xmlNode{peakFactor} cannot be specified.
+          See \url{https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.beta.html} for more details. """,
+        contentType=InputTypes.FloatType))
+    inputSpecification.addSub(InputData.parameterInputFactory("beta",
+        descr=r"""second shape parameter. If specified, \xmlNode{alpha} must also be provided, and
+          \xmlNode{peakFactor} cannot be specified.
+          See \url{https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.beta.html} for more details. """,
+        contentType=InputTypes.FloatType))
+    inputSpecification.addSub(InputData.parameterInputFactory("peakFactor",
+        descr=r"""alternate shape parameter. If specified, neither \xmlNode{alpha} nor \xmlNode{beta} may be specified.
+          """,
+        contentType=InputTypes.FloatType))
 
     return inputSpecification
 
@@ -1119,9 +1195,20 @@ class cls.
         specifying input of cls.
     """
     inputSpecification = super(Triangular, cls).getInputSpecification()
-    inputSpecification.addSub(InputData.parameterInputFactory("apex", contentType=InputTypes.FloatType))
-    inputSpecification.addSub(InputData.parameterInputFactory("min", contentType=InputTypes.FloatType))
-    inputSpecification.addSub(InputData.parameterInputFactory("max", contentType=InputTypes.FloatType))
+    inputSpecification.description = r"""classical triangular distribution. The probability density function for the
+      triangular distribution is given by
+      $f(x)=\frac{2(x-a)}{(b-a)(c-a))}$ for $a \le x < c$, and $\frac{2(b-x)}{(b-a)(b-c)}$ for $c< x \le b$, and 0 otherwise.
+      See \url{https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.triang.html} for more details.
+      """
+    inputSpecification.addSub(InputData.parameterInputFactory("min",
+        descr=r"""lower domain boundary of this distribution, referred to as $a$ in equation form.""",
+        contentType=InputTypes.FloatType))
+    inputSpecification.addSub(InputData.parameterInputFactory("max",
+        descr=r"""upper domain boundary of this distribution, referred to as $b$ in equation form.""",
+        contentType=InputTypes.FloatType))
+    inputSpecification.addSub(InputData.parameterInputFactory("apex",
+        descr=r"""location of the peak of the distribution, referred to as $c$ in equation form.""",
+        contentType=InputTypes.FloatType))
 
     return inputSpecification
 
@@ -1237,7 +1324,14 @@ class cls.
         specifying input of cls.
     """
     inputSpecification = super(Poisson, cls).getInputSpecification()
-    inputSpecification.addSub(InputData.parameterInputFactory("mu", contentType=InputTypes.FloatType))
+    inputSpecification.description = r"""classical Poisson discrete distribution. The probability mass function for the
+      Poisson distribution is given by
+      $f(k)=\frac{\mu^k e^{-\mu}}{k!}$ where $k$ is the number of occurances and $\mu$ is the mean.
+      See \url{https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.poisson.html} for more details.
+      """
+    inputSpecification.addSub(InputData.parameterInputFactory("mu",
+        descr=r"""mean rate of events/time""",
+        contentType=InputTypes.FloatType))
 
     return inputSpecification
 
@@ -1331,8 +1425,18 @@ class cls.
         specifying input of cls.
     """
     inputSpecification = super(Binomial, cls).getInputSpecification()
-    inputSpecification.addSub(InputData.parameterInputFactory("n", contentType=InputTypes.IntegerType))
-    inputSpecification.addSub(InputData.parameterInputFactory("p", contentType=InputTypes.FloatType))
+    inputSpecification.description = r"""classical binomial discrete distribution. The probability mass function for the
+      binomial distribution is given by
+      $f(k;n,p)=\binom{n}{k}p^k (1-p)^{n-k}$ where $k$ is the number of occurances, $p$ is the probabilty of occurance,
+      and $n$ is the number of experiments.
+      See \url{https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.binom.html} for more details.
+      """
+    inputSpecification.addSub(InputData.parameterInputFactory("n",
+        descr=r"""number of experiments or trials.""",
+        contentType=InputTypes.IntegerType))
+    inputSpecification.addSub(InputData.parameterInputFactory("p",
+        descr=r"""probability of occurance, often probability of a success.""",
+        contentType=InputTypes.FloatType))
 
     return inputSpecification
 
@@ -1433,7 +1537,14 @@ class cls.
         specifying input of cls.
     """
     inputSpecification = super(Bernoulli, cls).getInputSpecification()
-    inputSpecification.addSub(InputData.parameterInputFactory("p", contentType=InputTypes.FloatType))
+    inputSpecification.description = r"""classical Bernoulli discrete distribution. The probability mass function for the
+      Bernoulli distribution is given by
+      $f(k;p)=p$ if $k=1$ and $f(k;p)=1-p$ if $k=0$, where $k$ is the possible outcomes and $p$ is the probabilty of occurance.
+      See \url{https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.Bernoulli.html} for more details.
+      """
+    inputSpecification.addSub(InputData.parameterInputFactory("p",
+        descr=r"""probability of occurrance, or success.""",
+        contentType=InputTypes.FloatType))
 
     return inputSpecification
 
@@ -1526,7 +1637,14 @@ class cls.
         specifying input of cls.
     """
     inputSpecification = super(Geometric, cls).getInputSpecification()
-    inputSpecification.addSub(InputData.parameterInputFactory("p", contentType=InputTypes.FloatType))
+    inputSpecification.description = r"""classical geometric discrete distribution. The probability mass function for the
+      geometric distribution is given by
+      $f(k;p)=(1-p)^{k}p$, where $k$ is the possible outcomes and $p$ is the probabilty of occurance.
+      See \url{https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.geom.html} for more details.
+      """
+    inputSpecification.addSub(InputData.parameterInputFactory("p",
+        descr=r"""probability of occurrance, or success.""",
+        contentType=InputTypes.FloatType))
 
     return inputSpecification
 
@@ -1617,19 +1735,29 @@ def getInputSpecification(cls):
         specifying input of cls.
     """
     inputSpecification = InputData.parameterInputFactory(cls.__name__, ordered=True, baseNode=None)
-
-    StatePartInput = InputData.parameterInputFactory("state", contentType=InputTypes.FloatType)
-    StatePartInput.addParam("outcome", InputTypes.FloatOrStringType, True)
+    inputSpecification.description = r"""classical categorical discrete distribution, sometimes also referred to
+      as a multinomial distribution. The categorical distribution
+      describes the result of a random variable that can have $K$ possible outcome states, with each outcome potentially
+      having a distinct probability. These states can be numbers as well as strings.
+      """
+    StatePartInput = InputData.parameterInputFactory("state",
+        descr=r"""probability of this state's outcome""",
+        contentType=InputTypes.FloatType)
+    StatePartInput.addParam("outcome",
+        InputTypes.FloatOrStringType,
+        True,
+        descr=r"""value of this state's outcome""")
     inputSpecification.addSub(StatePartInput, InputData.Quantity.one_to_infinity)
     inputSpecification.addSub(InputData.parameterInputFactory("rtol",
-                                                              contentType=InputTypes.FloatType,
-                                                              descr=r"""Relative tolerance used to identify close state in case of"""
-                                                              r""" float/int states. Not used for string states!""", default=1e-6))
-
+        contentType=InputTypes.FloatType,
+        descr=r"""Relative tolerance used to identify close states in case of
+          float/int states. Not used for string states!""",
+        default=1e-6))
 
     ## Because we do not inherit from the base class, we need to manually
     ## add the name back in.
-    inputSpecification.addParam("name", InputTypes.StringType, True)
+    inputSpecification.addParam("name", InputTypes.StringType, True,
+        descr=r"""User-defined name to designate this entity in the RAVEN input file.""")
 
     return inputSpecification
 
@@ -2040,14 +2168,29 @@ class cls.
         specifying input of cls.
     """
     inputSpecification = InputData.parameterInputFactory(cls.__name__, ordered=True, baseNode=None)
-
-    StatePartInput = InputData.parameterInputFactory("state", contentType=InputTypes.StringType)
-    StatePartInput.addParam("outcome", InputTypes.FloatType, True)
-    StatePartInput.addParam("index", InputTypes.IntegerType, True)
-    TransitionInput = InputData.parameterInputFactory("transition", contentType=InputTypes.StringType)
+    inputSpecification.description = r"""Markov categorical discrete distribution. Describes a random variable
+      that can have $K$ possible outcomes, based on the steady state probilities provided by a Markov model.
+      """
+
+    StatePartInput = InputData.parameterInputFactory("state",
+        descr=r"""probability of occurrance, or outcome 1.""",
+        contentType=InputTypes.StringType)
+    StatePartInput.addParam("outcome",
+        InputTypes.FloatType,
+        True,
+        descr=r"""value of this outcome""")
+    StatePartInput.addParam("index",
+        InputTypes.IntegerType,
+        True,
+        descr=r"""indexes steady state probabilities corresponding to the transition matrix""")
+    TransitionInput = InputData.parameterInputFactory("transition",
+        descr=r"""transition matrix of the desired Markov model""",
+        contentType=InputTypes.StringType)
     inputSpecification.addSub(StatePartInput, InputData.Quantity.one_to_infinity)
     inputSpecification.addSub(TransitionInput, InputData.Quantity.zero_to_one)
-    inputSpecification.addSub(InputData.parameterInputFactory("workingDir", contentType=InputTypes.StringType))
+    inputSpecification.addSub(InputData.parameterInputFactory("workingDir",
+        descr=r"""filesystem referential path""",
+        contentType=InputTypes.StringType))
     ## Because we do not inherit from the base class, we need to manually
     ## add the name back in.
     inputSpecification.addParam("name", InputTypes.StringType, True)
@@ -2174,8 +2317,17 @@ class cls.
         specifying input of cls.
     """
     inputSpecification = super(Logistic, cls).getInputSpecification()
-    inputSpecification.addSub(InputData.parameterInputFactory("location", contentType=InputTypes.FloatType))
-    inputSpecification.addSub(InputData.parameterInputFactory("scale", contentType=InputTypes.FloatType))
+    inputSpecification.description = r"""classical logistic distribution. The probability density function for the
+      logistic distribution is given by
+      $f(x)=\frac{1}{1+e^{-\frac{x-\lambda}{\sigma}}}$ for $-\infty \leq x < \infty$.
+      See \url{https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.logistic.html} for more details.
+      """
+    inputSpecification.addSub(InputData.parameterInputFactory("location",
+        descr=r"""location parameter, referred to as $\lambda$ in equation form.""",
+        contentType=InputTypes.FloatType))
+    inputSpecification.addSub(InputData.parameterInputFactory("scale",
+        descr=r"""scale parameter, referred to as $\sigma$ in equation form.""",
+        contentType=InputTypes.FloatType))
 
     return inputSpecification
 
@@ -2284,8 +2436,17 @@ class cls.
         specifying input of cls.
     """
     inputSpecification = super(Laplace, cls).getInputSpecification()
-    inputSpecification.addSub(InputData.parameterInputFactory("location", contentType=InputTypes.FloatType))
-    inputSpecification.addSub(InputData.parameterInputFactory("scale", contentType=InputTypes.FloatType))
+    inputSpecification.description = r"""classical Laplace distribution. The probability density function for the
+      Laplace distribution is given by
+      $f(x)=\frac{1}{2b}e^{-\frac{\left| x-\mu\right| }{b}}$ for $-\infty \leq x \leq \infty$.
+      See \url{https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.laplace.html} for more details.
+      """
+    inputSpecification.addSub(InputData.parameterInputFactory("location",
+        descr=r"""location parameter, referred to as $\mu$ in equation form.""",
+        contentType=InputTypes.FloatType))
+    inputSpecification.addSub(InputData.parameterInputFactory("scale",
+        descr=r"""scale parameter, referred to as $b$ in equation form.""",
+        contentType=InputTypes.FloatType))
 
     return inputSpecification
 
@@ -2387,8 +2548,17 @@ class cls.
         specifying input of cls.
     """
     inputSpecification = super(Exponential, cls).getInputSpecification()
-    inputSpecification.addSub(InputData.parameterInputFactory("low", contentType=InputTypes.FloatType))
-    inputSpecification.addSub(InputData.parameterInputFactory("lambda", contentType=InputTypes.FloatType))
+    inputSpecification.description = r"""classical exponential distribution. The probability density function for the
+      exponential distribution is given by
+      $f(x)=\lambda e^{-\lambda x}$ for $0 \leq x \leq \infty$.
+      See \url{https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.expon.html} for more details.
+      """
+    inputSpecification.addSub(InputData.parameterInputFactory("low",
+        descr=r"""lower domain boundary for this distribution""",
+        contentType=InputTypes.FloatType))
+    inputSpecification.addSub(InputData.parameterInputFactory("lambda",
+        descr=r"""rate parameter for this distribution, shown as $\lambda$ in equation form""",
+        contentType=InputTypes.FloatType))
 
     return inputSpecification
 
@@ -2525,9 +2695,20 @@ class cls.
         specifying input of cls.
     """
     inputSpecification = super(LogNormal, cls).getInputSpecification()
-    inputSpecification.addSub(InputData.parameterInputFactory("mean", contentType=InputTypes.FloatType))
-    inputSpecification.addSub(InputData.parameterInputFactory("sigma", contentType=InputTypes.FloatType))
-    inputSpecification.addSub(InputData.parameterInputFactory("low", contentType=InputTypes.FloatType))
+    inputSpecification.description = r"""log-normal distribution. The probability density function for the
+      log-normal distribution is given by
+      $f(x)=\frac{1}{x\sigma\sqrt{2\pi}}e^{-\frac{(\ln{x}-\mu)^2}{2\sigma^2}}$ for $0 \leq x \leq \infty$.
+      See \url{https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.lognorm.html} for more details.
+      """
+    inputSpecification.addSub(InputData.parameterInputFactory("mean",
+        descr=r"""mean of log of the distribution""",
+        contentType=InputTypes.FloatType))
+    inputSpecification.addSub(InputData.parameterInputFactory("sigma",
+        descr=r"""standard deviation of the log of the distribution""",
+        contentType=InputTypes.FloatType))
+    inputSpecification.addSub(InputData.parameterInputFactory("low",
+        descr=r"""distribution lower domain boundary""",
+        contentType=InputTypes.FloatType))
 
     return inputSpecification
 
@@ -2642,9 +2823,20 @@ class cls.
         specifying input of cls.
     """
     inputSpecification = super(Weibull, cls).getInputSpecification()
-    inputSpecification.addSub(InputData.parameterInputFactory("low", contentType=InputTypes.FloatType))
-    inputSpecification.addSub(InputData.parameterInputFactory("k", contentType=InputTypes.FloatType))
-    inputSpecification.addSub(InputData.parameterInputFactory("lambda", contentType=InputTypes.FloatType))
+    inputSpecification.description = r"""Weibull distribution. The probability density function for the
+      Weibull distribution is given by
+      $f(x)=\frac{k}{\lambda}\left(\frac{x}{\lambda}\right)^{k-1} e^{-(x/\lambda)^k}$ for $0 \leq x \leq \infty$.
+      See \url{https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.weibull_min.html} for more details.
+      """
+    inputSpecification.addSub(InputData.parameterInputFactory("low",
+        descr=r"""distribution lower domain boundary""",
+        contentType=InputTypes.FloatType))
+    inputSpecification.addSub(InputData.parameterInputFactory("k",
+        descr=r"""shape parameter""",
+        contentType=InputTypes.FloatType))
+    inputSpecification.addSub(InputData.parameterInputFactory("lambda",
+        descr=r"""scale parameter""",
+        contentType=InputTypes.FloatType))
 
     return inputSpecification
 
@@ -2759,11 +2951,24 @@ class cls.
         specifying input of cls.
     """
     inputSpecification = super(Custom1D, cls).getInputSpecification()
-    inputSpecification.addSub(InputData.parameterInputFactory("workingDir", contentType=InputTypes.StringType))
-    inputSpecification.addSub(InputData.parameterInputFactory("functionType", contentType=InputTypes.StringType))
-    inputSpecification.addSub(InputData.parameterInputFactory("dataFilename", contentType=InputTypes.StringType))
-    inputSpecification.addSub(InputData.parameterInputFactory("functionID", contentType=InputTypes.StringType))
-    inputSpecification.addSub(InputData.parameterInputFactory("variableID", contentType=InputTypes.StringType))
+    inputSpecification.description = r"""Custom user-defined distribution. This allows empirically-defined
+        functions that are not currently defined in RAVEN to be used for probability weighting and sampling.
+        The distribution is defined through empirical distribution in a CSV file."""
+    inputSpecification.addSub(InputData.parameterInputFactory("workingDir",
+        descr=r"""relative working directory that contains the input file""",
+        contentType=InputTypes.StringType))
+    inputSpecification.addSub(InputData.parameterInputFactory("functionType",
+        descr=r"""type of initialization values specifid in the input file (pdf or cdf)""",
+        contentType=InputTypes.StringType))
+    inputSpecification.addSub(InputData.parameterInputFactory("dataFilename",
+        descr=r"""name of file to be used to initialize the distribution""",
+        contentType=InputTypes.StringType))
+    inputSpecification.addSub(InputData.parameterInputFactory("functionID",
+        descr=r"""ID of the function associated to the variableID in the input file""",
+        contentType=InputTypes.StringType))
+    inputSpecification.addSub(InputData.parameterInputFactory("variableID",
+        descr=r"""ID of the variable in the input file""",
+        contentType=InputTypes.StringType))
 
     return inputSpecification
 
@@ -2942,9 +3147,16 @@ class cls.
         specifying input of cls.
     """
     inputSpecification = super(LogUniform, cls).getInputSpecification()
+    inputSpecification.description = r"""Log-Uniform distribution. This distribution is associated
+        to a variable $x'$ such that $x = e^{x'}$. Sometimes known as the Reciprocal distribution. The
+        probability density function of this distribution is
+        $f(x)=\frac{1}{x(\ln{b}-\ln{a})}$ for $a \le x \le b$."""
 
     BaseInputType = InputTypes.makeEnumType("base", "baseType", ["natural","decimal"])
-    inputSpecification.addSub(InputData.parameterInputFactory("base"      , BaseInputType))
+    inputSpecification.addSub(InputData.parameterInputFactory("base",
+        BaseInputType,
+        descr=r"""exponent base for scaling the underlying uniform distribution, either "decimal" (base 10) or
+        "natural" base ($e$)."""))
 
     return inputSpecification
 
@@ -3059,7 +3271,9 @@ class cls.
         specifying input of cls.
     """
     inputSpecification = super(NDimensionalDistributions, cls).getInputSpecification()
-    inputSpecification.addSub(InputData.parameterInputFactory("workingDir", contentType=InputTypes.StringType))
+    inputSpecification.addSub(InputData.parameterInputFactory("workingDir",
+        descr=r"""relative working directory that contains data files""",
+        contentType=InputTypes.StringType))
 
     return inputSpecification
 
@@ -3183,13 +3397,22 @@ class cls.
         specifying input of cls.
     """
     inputSpecification = super(NDInverseWeight, cls).getInputSpecification()
-
-
-    DataFilenameParameterInput = InputData.parameterInputFactory("dataFilename", contentType=InputTypes.StringType)
-    DataFilenameParameterInput.addParam("type", InputTypes.StringType, True)
+    inputSpecification.description = r"""Custom user-defined N-dimensional distribution. This allows empirically-defined
+        custom distributions that are not currently defined in RAVEN to be used for probability weighting and sampling.
+        The combined distribution is defined through empirical distribution in a CSV file."""
+    DataFilenameParameterInput = InputData.parameterInputFactory("dataFilename",
+        descr=r"""name of file to be used to initialize the distribution""",
+        contentType=InputTypes.StringType)
+    DataFilenameParameterInput.addParam("type",
+        InputTypes.StringType,
+        True,
+        descr=r"""type of initialization values specifid in the input file (PDF or CDF)""")
     inputSpecification.addSub(DataFilenameParameterInput)
 
-    inputSpecification.addSub(InputData.parameterInputFactory("p", contentType=InputTypes.FloatType))
+    inputSpecification.addSub(InputData.parameterInputFactory("p",
+        descr=r"""power parameter. Greater values of p assign greater influence to values closest to
+          interpolation points.""",
+        contentType=InputTypes.FloatType))
 
     return inputSpecification
 
@@ -3385,9 +3608,17 @@ class cls.
         specifying input of cls.
     """
     inputSpecification = super(NDCartesianSpline, cls).getInputSpecification()
-
-    DataFilenameParameterInput = InputData.parameterInputFactory("dataFilename", contentType=InputTypes.StringType)
-    DataFilenameParameterInput.addParam("type", InputTypes.StringType, True)
+    inputSpecification.description = r"""describes a N-dimensional distribution given a set of points regularly
+        distributed on a Cartesian grid. These points sample the PDF of the original distribution. Distributed
+        values (PDF or CDF) are calculated using the ND Spline interpolation scheme."""
+
+    DataFilenameParameterInput = InputData.parameterInputFactory("dataFilename",
+        descr=r"""name of file to be used to initialize the distribution""",
+        contentType=InputTypes.StringType)
+    DataFilenameParameterInput.addParam("type",
+        InputTypes.StringType,
+        True,
+        descr=r"""type of initialization values specifid in the input file (PDF or CDF)""")
     inputSpecification.addSub(DataFilenameParameterInput)
 
     return inputSpecification
@@ -3555,14 +3786,33 @@ def getInputSpecification(cls):
       @ Out, inputSpecification, InputData.ParameterInput, class to use for input data.
     """
     inputSpecification = super(MultivariateNormal, cls).getInputSpecification()
-
-    MuListParameterInput = InputData.parameterInputFactory("mu", contentType=InputTypes.StringType)
-
-    CovarianceListParameterInput = InputData.parameterInputFactory("covariance", contentType=InputTypes.StringType)
-    CovarianceListParameterInput.addParam("type", InputTypes.StringType, False)
-
-    TransformationParameterInput = InputData.parameterInputFactory("transformation")
-    RankParameterInput = InputData.parameterInputFactory("rank", contentType=InputTypes.IntegerType)
+    inputSpecification.description = r"""describes a N-dimensional multivariate Gaussian normal distribution.
+        This generalizes the univariate normal distribution to higher dimensions. The multivariate normal
+        distribution is defined by an N-dimensional random vector $\widehat{x}$ and is defined by a multidimensional
+        mean $\widehat{\mu}$ and standard deviation $\Sigma$. The probability density function for this distribution is
+        $f(\widehat{x})=\frac{1}{\sqrt{(2\pi)^k}\left|\Sigma\right| } e^{-\frac{1}{2}(\widehat{x}-\widehat{\mu})^T \Sigma^{-1}(\widehat{x}-\widehat{\mu})}$."""
+
+    MuListParameterInput = InputData.parameterInputFactory("mu",
+        descr=r"""list of mean values for each dimension of the distribution""",
+        contentType=InputTypes.StringType)
+
+    CovarianceListParameterInput = InputData.parameterInputFactory("covariance",
+        descr=r"""list of covariance values in the covariance matrix. These are specified based on the \xmlAttr{type} parameter""",
+        contentType=InputTypes.StringType)
+    CovarianceListParameterInput.addParam("type",
+        InputTypes.StringType,
+        False,
+        descr=r"""type of covariance. \xmlString{abs} indicates a normal covariance matrix, while \xmlString{rel}
+        indicates a relative covarience matrics.
+        For \xmlNode{transformation}, \xmlString{pca} can be combined with both types, and
+        \xmlString{spline} only accepts \xmlString{abs}\default{abs}""")
+
+    TransformationParameterInput = InputData.parameterInputFactory("transformation",
+        descr=r"""enables input parameter transformation using principle component analysis (PCA). If enabled,
+        PCA is used on the input covariance matrix with truncation to the specified rank.""")
+    RankParameterInput = InputData.parameterInputFactory("rank",
+        descr=r"""desired dimensionality reduction for covariance matrix""",
+        contentType=InputTypes.IntegerType)
     TransformationParameterInput.addSub(RankParameterInput)
 
     inputSpecification.addSub(MuListParameterInput)
@@ -3570,7 +3820,9 @@ def getInputSpecification(cls):
     inputSpecification.addSub(TransformationParameterInput)
 
     MultivariateMethodType = InputTypes.makeEnumType("multivariateMethod","multivariateMethodType",["pca","spline"])
-    inputSpecification.addParam("method", MultivariateMethodType, True)
+    inputSpecification.addParam("method",
+        MultivariateMethodType,
+        True)
 
     return inputSpecification