The normalization methods will depend on the methods to generate and analyze the data and the biases that exist and accumulate in the generation of the data. It would be good if you tell people what sort of data is contained in the BAM file, and if the BAM files! have a relation to each other.
peak calling: unless the read numbers are not too different, the peak caller will normalize input and IP track. manual downsampling is also an option
generation of bigwig tracks to visualize: read count normalization to RPM, optionally extend reads in read direction by fragment length (MACS can do this for you)
there are other normalization methods to generate more "even" tracks that take into account GC content or similar, but they are not in widespread use.
Quantitative differences e.g. diffbind have their own normalization methods.
.... I guess there are other use cases and then you would do something else entirely
The normalization methods will depend on the methods to generate and analyze the data and the biases that exist and accumulate in the generation of the data. It would be good if you tell people what sort of data is contained in the BAM file, and if the BAM files! have a relation to each other.
I made an edit, I hope it helps in answering the question.
Do you mean, read density normalization??
yes, I mean read density normalization.
I am not sure about bam file but had you already thought of MA plot?