Wikipedia entry on likelihood seems ambiguous I have a simple question regarding "conditional probability" and "Likelihood". (I have already surveyed this question here but to no avail.)
It starts from the Wikipedia page on likelihood. They say this:

The likelihood of a set of parameter values, $\theta$, given
  outcomes $x$, is equal to the probability of those observed outcomes
  given those parameter values, that is
$$\mathcal{L}(\theta \mid x) = P(x \mid \theta)$$

Great! So in English, I read this as: "The likelihood of parameters equaling theta, given data X = x, (the left-hand-side), is equal to the probability of the data X being equal to x, given that the parameters are equal to theta". (Bold is mine for emphasis).
However, no less than 3 lines later on the same page, the Wikipedia entry then goes on to say:

Let $X$ be a random variable with a discrete probability distribution
  $p$ depending on a parameter $\theta$. Then the function
$$\mathcal{L}(\theta \mid x) = p_\theta (x) = P_\theta (X=x), \, $$
considered as a function of $\theta$, is called the likelihood
  function (of $\theta$, given the outcome $x$ of the random variable
  $X$). Sometimes the probability of the value $x$ of $X$ for the
  parameter value $\theta$ is written as $P(X=x\mid\theta)$; often
  written as $P(X=x;\theta)$ to emphasize that this differs from
  $\mathcal{L}(\theta \mid x) $ which is not a conditional probability,
  because $\theta$ is a parameter and not a random variable.

(Bold is mine for emphasis). So, in the first quote, we are literally told about a conditional probability of $P(x\mid\theta)$, but immediately afterwards, we are told that this is actually NOT a conditional probability, and should be in fact written as $P(X = x; \theta)$? 
So, which one is is? Does the likelihood actually connote a conditional probability ala the first quote? Or does it connote a simple probability ala the second quote?
EDIT:
Based on all the helpful and insightful answers I have received thus far, I have summarized my question - and my understanding thus far as so: 


*

*In English, we say that: "The likelihood is a function of parameters, GIVEN the observed data." In math, we write it as: $L(\mathbf{\Theta}= \theta \mid \mathbf{X}=x)$.

*The likelihood is not a probability. 

*The likelihood is not a probability distribution. 

*The likelihood is not a probability mass.

*The likelihood is however, in English: "A product of probability distributions, (continuous case), or a product of probability masses, (discrete case), at where $\mathbf{X} = x$, and parameterized by $\mathbf{\Theta}= \theta$." In math, we then write it as such: $L(\mathbf{\Theta}= \theta \mid \mathbf{X}=x) = f(\mathbf{X}=x ; \mathbf{\Theta}= \theta) $ (continuous case, where $f$ is a PDF), and as
$L(\mathbf{\Theta}= \theta \mid \mathbf{X}=x) = P(\mathbf{X}=x ; \mathbf{\Theta}= \theta) $ (discrete case, where $P$ is a probability mass). The takeaway here is that at no point here whatsoever is a conditional probability coming into play at all. 

*In Bayes theorem, we have: $P(\mathbf{\Theta}= \theta \mid \mathbf{X}=x) = \frac{P(\mathbf{X}=x \mid \mathbf{\Theta}= \theta) \ P(\mathbf{\Theta}= \theta)}{P(\mathbf{X}=x)}$. Colloquially, we are told that "$P(\mathbf{X}=x \mid  \mathbf{\Theta}= \theta)$ is a likelihood", however, this is not true, since $\mathbf{\Theta}$ might be an actual random variable. Therefore, what we can correctly say however, is that this term $P(\mathbf{X}=x \mid \mathbf{\Theta}= \theta)$ is simply "similar" to a likelihood. (?) [On this I am not sure.]


EDIT II:
Based on @amoebas answer, I have drawn his last comment. I think it's quite elucidating, and I think it clears up the main contention I was having. (Comments on the image).

EDIT III: 
I extended @amoebas comments to the Bayesian case just now as well:

 A: There are several aspects of the common descriptions of likelihood that are imprecise or omit detail in a way that engenders confusion. The Wikipedia entry is a good example.
First,  likelihood cannot be generally equal to a the probability of the data given the parameter value, as likelihood is only defined up to a proportionality constant. Fisher was explicit about that when he first formalised likelihood (Fisher, 1922). The reason for that seems to be the fact that there is no restraint on the integral (or sum) of a likelihood function, and the probability of observing data $x$ within a statistical model given any value of the parameter(s) is strongly affected by the precision of the data values and of the granularity of specification of the parameter values.
Second, it is more helpful to think about the likelihood function than individual likelihoods. The likelihood function is a function of the model parameter value(s), as is obvious from a graph of a likelihood function. Such a graph also makes it easy to see that the likelihoods allow a ranking of the various values of the parameter(s) according to how well the model predicts the data when set to those parameter values. Exploration of likelihood functions makes the roles of the data and the parameter values much more clear, in my opinion, than can cogitation of the various formulas given in the original question.
The use a ratio of pairs of likelihoods within a likelihood function as the relative degree of support offered by the observed data for the parameter values (within the model) gets around the problem of unknown proportionality constants because those constants cancel in the ratio. It is important to note that the constants would not necessarily cancel in a ratio of likelihoods that come from separate likelihood functions (i.e. from different statistical models).
Finally, it is useful to be explicit about the role of the statistical model because likelihoods are determined by the statistical model as well as the data. If you choose a different model you get a different likelihood function, and you can get a different unknown proportionality constant.
Thus, to answer the original question, likelihoods are not a probability of any sort. They do not obey Kolmogorov's axioms of probability, and they play a different role in statistical support of inference from the roles played by the various types of probability. 


*

*Fisher (1922) On the mathematical foundations of statistics http://rsta.royalsocietypublishing.org/content/222/594-604/309
A: Wikipedia should have said that $L(\theta)$ is not a conditional probability of $\theta$ being in some specified set, nor a probability density of $\theta$.  Indeed, if there are infinitely many values of $\theta$ in the parameter space, you can have
$$
\sum_\theta L(\theta) = \infty,
$$
for example by having $L(\theta)=1$ regardless of the value of $\theta$, and if there is some standard measure $d\theta$ on the parameter space $\Theta$, then in the same way one can have
$$
\int_\Theta L(\theta)\,d\theta =\infty.
$$
An essential point that the article should emphasize is that $L$ is the function
$$
\theta \mapsto P(x\mid\theta) \text{ and NOT } x\mapsto P(x\mid\theta).
$$
A: 
"I read this as: "The likelihood of parameters equaling theta, given
  data X = x, (the left-hand-side), is equal to the probability of the
  data X being equal to x, given that the parameters are equal to
  theta". (Bold is mine for emphasis)."

It's the probability of the set of observations given the parameter is theta. This is perhaps confusing because they write $P(x|\theta)$ but then $\mathcal{L}(\theta|x)$. 
The explanation (somewhat objectively) implies that $\theta$ is not a random variable. It could, for example, be a random variable with some prior distribution in a Bayesian setting. The point however, is that we suppose $\theta=\theta$, a concrete value and then make statements about the likelihood of our observations. This is because there is only one true value of $\theta$ in whatever system we're interested in.
A: I think this is largely unnecessary splitting hairs.
Conditional probability $P(x\mid y)\equiv P(X=x \mid Y=y)$ of $x$ given $y$ is defined for two random variables $X$ and $Y$ taking values $x$ and $y$. But we can also talk about probability $P(x\mid\theta)$ of $x$ given $\theta$ where $\theta$ is not a random variable but a parameter.
Note that in both cases the same term "given" and the same notation $P(\cdot\mid\cdot)$ can be used. There is no need to invent different notations. Moreover, what is called "parameter" and what is called "random variable" can depend on your philosophy, but the math does not change.
The first quote from Wikipedia states that $\mathcal{L}(\theta \mid x) = P(x \mid \theta)$ by definition. Here it is assumed that $\theta$ is a parameter. The second quote says that $\mathcal{L}(\theta \mid x)$ is not a conditional probability. This means that it is not a conditional probability of $\theta$ given $x$; and indeed it cannot be, because $\theta$ is assumed to be a parameter here.
In the context of Bayes theorem $$P(a\mid b)=\frac{P(b\mid a)P(a)}{P(b)},$$ both $a$ and $b$ are random variables. But we can still call $P(b\mid a)$ "likelihood" (of $a$), and now it is also a bona fide conditional  probability (of $b$). This terminology is standard in Bayesian statistics. Nobody says it is something "similar" to the likelihood; people simply call it the likelihood.
Note 1: In the last paragraph, $P(b\mid a)$ is obviously a conditional probability of $b$. As a likelihood $\mathcal L(a\mid b)$ it is seen as a function of $a$; but it is not a probability distribution (or conditional probability) of $a$! Its integral over $a$ does not necessarily equal $1$. (Whereas its integral over $b$ does.)
Note 2: Sometimes likelihood is defined up to an arbitrary proportionality constant, as emphasized by @MichaelLew (because most of the time people are interested in likelihood ratios). This can be useful, but is not always done and is not essential.

See also What is the difference between "likelihood" and "probability"? and in particular @whuber's answer there.
I fully agree with @Tim's answer in this thread too (+1).
A: You already got two nice answers, but since it still seems unclear for you let me provide one. Likelihood is defined as
$$ \mathcal{L}(\theta|X) = P(X|\theta)  = \prod_i f_\theta(x_i) $$
so we have likelihood of some parameter value $\theta$ given the data $X$. It is equal to product of probability mass (discrete case), or density (continuous case) functions $f$ of $X$ parametrized by $\theta$. Likelihood is a function of parameter given the data. Notice that $\theta$ is a parameter that we are optimizing, not a random variable, so it does not have any probabilities assigned to it. This is why Wikipedia states that using conditional probability notation may be ambiguous, since we are not conditioning on any random variable. On another hand, in Bayesian setting $\theta$ is a random variable and does have distribution, so we can work with it as with any other random variable and we can use Bayes theorem to calculate the posterior probabilities. Bayesian likelihood is still likelihood since it tells us about likelihood of data given the parameter, the only difference is that the parameter is considered as random variable.
If you know programming, you can think of likelihood function as of overloaded function in programming. Some programming languages allow you to have function that works differently when called using different parameter types. If you think of likelihood like this, then by default if takes as argument some parameter value and returns likelihood of data given this parameter. On another hand, you can use such function in Bayesian setting, where parameter is random variable, this leads to basically the same output, but that can be understood as conditional probability since we are conditioning on random variable. In both cases the function works the same, just you use it and understand it a little bit differently.
// likelihood "as" overloaded function
Default Likelihood(Numeric theta, Data X) {
    return f(X, theta); // returns likelihood, not probability
}

Bayesian Likelihood(RandomVariable theta, Data X) {
    return f(X, theta); // since theta is r.v., the output can be
                        // understood as conditional probability
}

Moreover, you rather won't find Bayesians who write Bayes theorem as 
$$ P(\theta|X) \propto \mathcal{L}(\theta|X) P(\theta) $$
...this would be very confusing. First, you would have $\theta|X$ on both sides of equation and it wouldn't have much sense. Second, we have posterior probability to know about probability of $\theta$ given data (i.e. the thing that you would like to know in likelihoodist framework, but you don't when $\theta$ is not a random variable). Third, since $\theta$ is a random variable, we have and write it as conditional probability. The $L$-notation is generally reserved for likelihoodist setting. The name likelihood is used by convention in both approaches to denote similar thing: how probability of observing such data changes given your model and the parameter.
